 |
 |
|---|
#CABIN VERIFICATION AND VALIDATION - BIOLOGICAL DATA
Environment and Climate Change Canada
Analysis performed on 2019-08-13 13:55:31
This report presents the results for verification and validation of a biological CABIN data file associated to the project Test.
In this analysis, the biological dataset is checked to answer the following question:
- Are the biological data reflecting the sites visited?
This document is a R notebook written in R Markdown. When you execute the code embedded in the notebook, the results will appear under the corresponding code. To do this, place your cursor inside a chunk (box which contains R code) and click the green arrow to the right of it named Run Current Chunk or press Ctrl+Shift+Enter (Cmd+Shift+Enter in macOS) on your keyboard. Repeat for each chunk. As the code contained in this notebook is executed, the results will appear under each of the corresponding chunks in this window. Once all commands are executed, click the Preview button at the top left of this window or press the Ctrl+Shift+K keys (Cmd+Shift+K in macOS). A new window will appear and will contain the report of these verification and validation results for the biological CABIN data.
##Requirements
##Descriptive Statistics
The data file contains 448 visits (lines) and 51 variables (columns).
The following table presents a subset of the data.
Reading Biological Data
Examine the following:
- Does the file seem to been read correctly?
- Are columns missing?
List of visits present in the biological data (ID from the CABIN database):
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448
- Compare the list above with the following table which shows the visits from all the data for this project.
Variables List:
Ameletidae, Ametropodidae, Aturidae, Baetidae, Baetiscidae, Brachycentridae, Capniidae, Ceratopogonidae, Chironomidae, Chloroperlidae, Edwardsiidae, Elmidae, Empididae, Enchytraeidae, Ephemerellidae, Ephemeridae, Feltriidae, Glossosomatidae, Heptageniidae, Hydrophilidae, Hydropsychidae, Hydroptilidae, Hydrozetidae, Hydryphantidae, Hygrobatidae, Lebertiidae, Lepidostomatidae, Leptoceridae, Leptohyphidae, Leptophlebiidae, Limnephilidae, Lumbriculidae, Naididae, Nemouridae, Oreoleptidae, Peltoperlidae, Perlidae, Perlodidae, Phryganeidae, Pionidae, Planorbidae, Poduridae, Polycentropodidae, Psychodidae, Sialidae, Simuliidae, Sperchontidae, Taeniopterygidae, Tipulidae, Torrenticolidae, Valvatidae
##General Statistics
The following table shows the main general parameters per variable. When the result on the BinaryData line of the array is TRUE, this indicates that the variable has binary data. However, this result can also be obtained because the variable has only one to two values at most. The Na.values line indicates the number of missing values for each variable.
Table of General Statistics
- Examine the data and make sure it reflects reality. Examine statistics and identify, if present, anomalies with statistics.
##Geographical Distribution
###Taxa Distribution
The following graphs show the location (latitude and longitude) ainsi que l’abondance en taxons des sites observées dans le fichier de données pour chaque taxon.
- Is the geographical distribution of taxa showing anomalies?
- Is there data that are odd?
###Richness Distribution
The following graphs show the location (latitude and longitude) and taxa richness on sites.
- Is the geographical distribution of richness showing anomalies?
- Are some richness measures out of the ordinary?
- Is there a pattern in taxa richness?
The following interactive map illustrates the same data as the above graph. Click on a point to show the taxa richness.
Warning in validateCoords(lng, lat, funcName): Data contains 7 rows with
either missing or invalid lat/lon values and will be ignored##Presence of Outliers
###Dispersion of Observed Values
The following scatterplots present the value of the taxa abundances observed in the dataset. Below the x-axis is the visit identifier for the corresponding observed value, namely the name of the site from which the data originated, its sampling date and the sampling number.
- Is the distribution of abundance values indicating an ecological phenomenon or a potential problem (disturbance)?
- Are some taxa very rare or very frequent?
- Is a problem with abundant data apparent?
###Boxplots of Observed and Transformed Values
The first boxplot (left) shows the distribution of continuous variable values observed in the data file, the middle illustrates the distribution of the logarithmic-transformed values, and the third box plot (right) shows the distribution of the square-root-transformed values.
- Do data seem out of the ordinary?
- Is a data transformation showing a better statistical distribution than raw data?
###Identification of potential outliers
The first boxplot (left) shows the distribution of abundance values observed in the dataset and the dispersion diagram (right) show the distribution of abundance values in the order in which they appear in this file. Data with identification by their identifier on the diagrams are potentially outliers contained in the dataset. The method used to identify potentially outliers is the interquantile range (IQR). The IQR is calculated as follows:
IQR(x) = quantile(x, 3/4) - quantile(x, 1/4)
Potential outliers are defined as values below Q1 - 1,5 IQR or above Q3 + 1,5 IQR.
The identifier of the visit corresponds to the name of the site from which the data originated, its sampling date and the sample number for this visit.
The interpretation of Cleveland dotplot is done by looking at points that stick out on the right-hand side, or on the left-hand side. They are observed values that are considerable larger, or smaller, than the majority of the observations, and require further investigation. When the most likely explanation is that the extreme observations are measurement errors, they should be dropped because their presence is likely to dominate the analysis. On the other hand, if the removal of these values are not an option, a data transformation should be considered.
- Are extreme values on the boxplots could be considered as outliers?
- Are those same points on Cleveland dotplot isolated (on the left or right)?
- Should some taxa (too rare or too abundant) not be considered in further analysis?
The following list shows another way of identifying potential outliers contained in the dataset for each variable. When the identifier of a data item is indicated therein, this indicates that this data represents potentially aberrant data contained in the data file. "" indicates non-aberrant data and NA indicates missing values. The identifier of the visit corresponds to the name of the site from which the data originated, its sampling date and the sampling number.
Ameletidae
Site, Date and Number, Is NA
Site2 2011-01-01 18807, No
SM03262 2008-09-29 19074, No
VA03272 2008-09-29 18740, No
Number of Potential Outliers: 3
Ametropodidae
Site, Date and Number, Is NA
TH10062 2008-09-29 18366, No
Number of Potential Outliers: 1
Aturidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Baetidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Baetiscidae
Site, Date and Number, Is NA
CJ9271 2008-09-30 18747, No
Number of Potential Outliers: 1
Brachycentridae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Capniidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 2
Ceratopogonidae
Site, Date and Number, Is NA
KV12191 2010-09-15 18310, No
MWLMCExp 2011-09-14 19547, No
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 3
Chironomidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Chloroperlidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Edwardsiidae
Site, Date and Number, Is NA
MG09031 2012-12-12 21909, No
Number of Potential Outliers: 1
Elmidae
Site, Date and Number, Is NA
KV12191 2010-09-15 18310, No
Number of Potential Outliers: 1
Empididae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Enchytraeidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
Number of Potential Outliers: 1
Ephemerellidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Ephemeridae
Site, Date and Number, Is NA
EE06181 2008-09-30 19037, No
MH02262 2008-09-29 16858, No
PM02041 2008-09-30 19071, No
Number of Potential Outliers: 3
Feltriidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
Number of Potential Outliers: 1
Glossosomatidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Heptageniidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Hydrophilidae
Site, Date and Number, Is NA
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 1
Hydropsychidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Hydroptilidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Hydrozetidae
Site, Date and Number, Is NA
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 1
Hydryphantidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Hygrobatidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
Number of Potential Outliers: 1
Lebertiidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Lepidostomatidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Leptoceridae
Site, Date and Number, Is NA
CD10141 2008-09-29 17522, No
Number of Potential Outliers: 1
Leptohyphidae
Site, Date and Number, Is NA
SM03262 2008-09-29 19074, No
VA03272 2008-09-29 18740, No
Number of Potential Outliers: 2
Leptophlebiidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Limnephilidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 2
Lumbriculidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 2
Naididae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Nemouridae
Site, Date and Number, Is NA
KV12191 2010-09-15 18310, No
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 2
Oreoleptidae
Site, Date and Number, Is NA
CJ9272 2008-09-29 18748, No
CJ9273 2008-09-28 18750, No
KL08313 2008-09-28 16868, No
LL10173 2008-09-28 18802, No
Number of Potential Outliers: 4
Peltoperlidae
Site, Date and Number, Is NA
CD10141 2008-09-29 17522, No
JJ06301 2008-09-30 17488, No
MH02261 2008-09-30 16852, No
Number of Potential Outliers: 3
Perlidae
Site, Date and Number, Is NA
JB12071 2008-09-30 19086, No
Number of Potential Outliers: 1
Perlodidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
Number of Potential Outliers: 1
Phryganeidae
Site, Date and Number, Is NA
KV12191 2010-09-15 18310, No
Number of Potential Outliers: 1
Pionidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Planorbidae
Site, Date and Number, Is NA
KV12191 2010-09-15 18310, No
Number of Potential Outliers: 1
Poduridae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
MWLWCRef 2011-09-11 19548, No
Number of Potential Outliers: 2
Polycentropodidae
Site, Date and Number, Is NA
SM03262 2008-09-29 19074, No
VA03272 2008-09-29 18740, No
Number of Potential Outliers: 2
Psychodidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Sialidae
Site, Date and Number, Is NA
KV12191 2010-09-15 18310, No
Number of Potential Outliers: 1
Simuliidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Sperchontidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Taeniopterygidae
Site, Date and Number, Is NA
MWLMCExp 2011-09-14 19547, No
Number of Potential Outliers: 1
Tipulidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Torrenticolidae
Site, Date and Number, Is NA
Number of Potential Outliers: 0
Valvatidae
Site, Date and Number, Is NA
KV12191 2010-09-15 18310, No
Number of Potential Outliers: 1 Ameletidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Ametropodidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Aturidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Baetidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Baetiscidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Brachycentridae
Site, Date and Number, Is NA
Number of Missing Values: 0
Capniidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Ceratopogonidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Chironomidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Chloroperlidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Edwardsiidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Elmidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Empididae
Site, Date and Number, Is NA
Number of Missing Values: 0
Enchytraeidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Ephemerellidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Ephemeridae
Site, Date and Number, Is NA
Number of Missing Values: 0
Feltriidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Glossosomatidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Heptageniidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Hydrophilidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Hydropsychidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Hydroptilidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Hydrozetidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Hydryphantidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Hygrobatidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Lebertiidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Lepidostomatidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Leptoceridae
Site, Date and Number, Is NA
Number of Missing Values: 0
Leptohyphidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Leptophlebiidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Limnephilidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Lumbriculidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Naididae
Site, Date and Number, Is NA
Number of Missing Values: 0
Nemouridae
Site, Date and Number, Is NA
Number of Missing Values: 0
Oreoleptidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Peltoperlidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Perlidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Perlodidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Phryganeidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Pionidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Planorbidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Poduridae
Site, Date and Number, Is NA
Number of Missing Values: 0
Polycentropodidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Psychodidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Sialidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Simuliidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Sperchontidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Taeniopterygidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Tipulidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Torrenticolidae
Site, Date and Number, Is NA
Number of Missing Values: 0
Valvatidae
Site, Date and Number, Is NA
Number of Missing Values: 0 ##Presence of zeros
In ecological studies, we need to consider what it means when two species are jointly absent. This could say something important about the ecological characteristics of a site. When two sites both have the same joint absences, this might mean that the sites are ecologically similar. On the other hand, if a species has a highly clumped distribution, or is simply rare, then joint absences might arise through chance and say nothing about the suitability of a given site for a species, the similarity among the habitat needs of species or the ecological similarity of sites. A high frequency of zeros, thus, can greatly complicate interpretation of such analyses.
###Abundance Frequency Distribution
Abundance values a taxa commonly show a skew distribution that looks like a log-normal distribution, with many small to moderate values and a few extremely large values.
The following histogram illustrates the abundance frequency distribution of taxa. This chart helps, among other things, to evaluate the number of 0 values in the dataset.
###Presence of double-zeros
We need to know whether there are double zeros in the data. This means that for each species-pair, we need to calculate how often both had zero abundance for the same site.
The following correlation matrix (or corrgram) illustrates the frequency with which pairs of taza both have zero abundance.The color and the amount that a circle has been filled correspond to the proportion of observations with double zeros. A white color indicates a correlation close to 0.5 whereas a dark blue color indicates a correlation near 1. This matrix also illustrates the proportion of correlation values. 0 abundances of a taxon relative to all abundance values observed in the latter. The diagonal running from bottom left to top right represents the percentage of observations of a taxa equal to zero. Only taxa present in more than 25% of the visits are shown.
- Are data showing a short or a long biological gradient?
- What the presence of double zeros means for the data?
##Data Normality
Various statistical techniques assume normality. It is important to know whether the statistical technique to be used does assume normality, and what exactly is assumed to be normally distributed? For example, a Principal component analysis (PCA) does not require normality. Linear regression does assume normality, but is reasonably robust against violation of the assumption. For other techniques like discriminant analysis, normality of observations of a particular variable within each group is important. Therefore, testing for normality (among other things) is always recommanded when starting the data analysis phase of an ecological project.
###Quantile-Quantile (Q-Q) plots and et frequency histograms
For each variable, the first graph (left) illustrates by points the observed distribution of taxon values and the theoretical normal distribution calculated from the parameters of the distribution observed by a line. The more the values observed are positioned on the right, the more these are distributed according to the normal law. The second graph (right) shows a histogram the frequency distribution of the values observed by taxon. It allows to verify and validate if the data distribution seems to follow the normal distribution. This histogram also illustrates the mean abundance per taxon by a solid line as well as the standard deviation of abundances per taxon by two dotted lines.
####Normality test by taxon
One way to examine the normality of data is to use the Snows test. For each taxon, this calculation tests the null hypothesis that the data comes from an exact normal population.
This is a much less interesting null hypothesis than what we usually want, which is to know if the data come from a distribution that is similar enough to the normal to use normal theory inference.
A value of P (p-value) less than 0.05 indicates that it is not possible to assume that the distribution of the data follows the normal distribution with a probability of 95%.
$Ameletidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Ametropodidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Aturidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Baetidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Baetiscidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Brachycentridae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Capniidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Ceratopogonidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Chironomidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Chloroperlidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Edwardsiidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Elmidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Empididae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Enchytraeidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Ephemerellidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Ephemeridae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Feltriidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Glossosomatidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Heptageniidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Hydrophilidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Hydropsychidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Hydroptilidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Hydrozetidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Hydryphantidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Hygrobatidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Lebertiidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Lepidostomatidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Leptoceridae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Leptohyphidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Leptophlebiidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Limnephilidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Lumbriculidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Naididae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Nemouridae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Oreoleptidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Peltoperlidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Perlidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Perlodidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Phryganeidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Pionidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Planorbidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Poduridae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Polycentropodidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Psychodidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Sialidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Simuliidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Sperchontidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Taeniopterygidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Tipulidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Torrenticolidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)
$Valvatidae
Snow's Penultimate Normality Test
data: newX[, i]
p-value < 2.2e-16
alternative hypothesis:
The data does not come from a strict normal distribution (but
may represent a distribution that is close enough)####Taxa Occurences
At how many sites does each taxon occur?
First, the sorted list below shows the accreasing occurence of taxa in the dataset.
After that, two graphs are proposed. The first graph (left) shows the distribution of taxa presence. The second graph (right) illustrates the log-transformed data. On these graphs, the mean occurence is shown by a solid line whereas standard deviation by dashed lines.
En examination of these two graphs allows us to verify if the taxon dataset seams to comply to a normal distribution. If the distribution in the second graph is closer to a normal distribution, a log transformation could be useful.
Ameletidae Ametropodidae Aturidae Baetidae
147 1 146 443
Baetiscidae Brachycentridae Capniidae Ceratopogonidae
1 148 2 3
Chironomidae Chloroperlidae Edwardsiidae Elmidae
444 150 1 1
Empididae Enchytraeidae Ephemerellidae Ephemeridae
288 1 294 3
Feltriidae Glossosomatidae Heptageniidae Hydrophilidae
1 294 440 1
Hydropsychidae Hydroptilidae Hydrozetidae Hydryphantidae
439 146 1 144
Hygrobatidae Lebertiidae Lepidostomatidae Leptoceridae
1 146 293 1
Leptohyphidae Leptophlebiidae Limnephilidae Lumbriculidae
146 293 2 2
Naididae Nemouridae Oreoleptidae Peltoperlidae
443 149 4 3
Perlidae Perlodidae Phryganeidae Pionidae
1 145 1 145
Planorbidae Poduridae Polycentropodidae Psychodidae
1 2 145 145
Sialidae Simuliidae Sperchontidae Taeniopterygidae
1 148 147 1
Tipulidae Torrenticolidae Valvatidae
293 145 1 [1] "Sorted list of taxa occurences" Ametropodidae Baetiscidae Edwardsiidae Elmidae
1 1 1 1
Enchytraeidae Feltriidae Hydrophilidae Hydrozetidae
1 1 1 1
Hygrobatidae Leptoceridae Perlidae Phryganeidae
1 1 1 1
Planorbidae Sialidae Taeniopterygidae Valvatidae
1 1 1 1
Capniidae Limnephilidae Lumbriculidae Poduridae
2 2 2 2
Ceratopogonidae Ephemeridae Peltoperlidae Oreoleptidae
3 3 3 4
Hydryphantidae Perlodidae Pionidae Polycentropodidae
144 145 145 145
Psychodidae Torrenticolidae Aturidae Hydroptilidae
145 145 146 146
Lebertiidae Leptohyphidae Ameletidae Sperchontidae
146 146 147 147
Brachycentridae Simuliidae Nemouridae Chloroperlidae
148 148 149 150
Empididae Lepidostomatidae Leptophlebiidae Tipulidae
288 293 293 293
Ephemerellidae Glossosomatidae Hydropsychidae Heptageniidae
294 294 439 440
Baetidae Naididae Chironomidae
443 443 444 
The following graphs are similar to the previous ones but for relative frequencies.
Ameletidae Ametropodidae Aturidae Baetidae
32.8125000 0.2232143 32.5892857 98.8839286
Baetiscidae Brachycentridae Capniidae Ceratopogonidae
0.2232143 33.0357143 0.4464286 0.6696429
Chironomidae Chloroperlidae Edwardsiidae Elmidae
99.1071429 33.4821429 0.2232143 0.2232143
Empididae Enchytraeidae Ephemerellidae Ephemeridae
64.2857143 0.2232143 65.6250000 0.6696429
Feltriidae Glossosomatidae Heptageniidae Hydrophilidae
0.2232143 65.6250000 98.2142857 0.2232143
Hydropsychidae Hydroptilidae Hydrozetidae Hydryphantidae
97.9910714 32.5892857 0.2232143 32.1428571
Hygrobatidae Lebertiidae Lepidostomatidae Leptoceridae
0.2232143 32.5892857 65.4017857 0.2232143
Leptohyphidae Leptophlebiidae Limnephilidae Lumbriculidae
32.5892857 65.4017857 0.4464286 0.4464286
Naididae Nemouridae Oreoleptidae Peltoperlidae
98.8839286 33.2589286 0.8928571 0.6696429
Perlidae Perlodidae Phryganeidae Pionidae
0.2232143 32.3660714 0.2232143 32.3660714
Planorbidae Poduridae Polycentropodidae Psychodidae
0.2232143 0.4464286 32.3660714 32.3660714
Sialidae Simuliidae Sperchontidae Taeniopterygidae
0.2232143 33.0357143 32.8125000 0.2232143
Tipulidae Torrenticolidae Valvatidae
65.4017857 32.3660714 0.2232143 [1] "Sorted list of taxa relative frequency" Ametropodidae Baetiscidae Edwardsiidae Elmidae
0.2 0.2 0.2 0.2
Enchytraeidae Feltriidae Hydrophilidae Hydrozetidae
0.2 0.2 0.2 0.2
Hygrobatidae Leptoceridae Perlidae Phryganeidae
0.2 0.2 0.2 0.2
Planorbidae Sialidae Taeniopterygidae Valvatidae
0.2 0.2 0.2 0.2
Capniidae Limnephilidae Lumbriculidae Poduridae
0.4 0.4 0.4 0.4
Ceratopogonidae Ephemeridae Peltoperlidae Oreoleptidae
0.7 0.7 0.7 0.9
Hydryphantidae Perlodidae Pionidae Polycentropodidae
32.1 32.4 32.4 32.4
Psychodidae Torrenticolidae Aturidae Hydroptilidae
32.4 32.4 32.6 32.6
Lebertiidae Leptohyphidae Ameletidae Sperchontidae
32.6 32.6 32.8 32.8
Brachycentridae Simuliidae Nemouridae Chloroperlidae
33.0 33.0 33.3 33.5
Empididae Lepidostomatidae Leptophlebiidae Tipulidae
64.3 65.4 65.4 65.4
Ephemerellidae Glossosomatidae Hydropsychidae Heptageniidae
65.6 65.6 98.0 98.2
Baetidae Naididae Chironomidae
98.9 98.9 99.1 
####Boxplots for taxa occurences
The first boxplot (left) illustrates the distribution of occurrences calculated from the data file whereas the second (right) shows the distribution of occurrences calculated from the log transformed data file.
The following boxplots are similar to the previous but for relative frequencies
##Taxa Independence
###Correlation matrix between taxa
The following correlation matrix shows the relationship between all pairs of taxa. It allows to verify the presence of co-occurence between taxa. Only taxa present in more than 25% of the visits are shown.
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 2s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 2s
plot: [2,2] [================>----------------------------] 38% est: 2s
plot: [2,3] [===================>-------------------------] 44% est: 2s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 2s
plot: [1,3] [=======>-------------------------------------] 19% est: 3s
plot: [1,4] [==========>----------------------------------] 25% est: 3s
plot: [2,1] [=============>-------------------------------] 31% est: 3s
plot: [2,2] [================>----------------------------] 38% est: 2s
plot: [2,3] [===================>-------------------------] 44% est: 2s
plot: [2,4] [=====================>-----------------------] 50% est: 2s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 2s
plot: [1,4] [==========>----------------------------------] 25% est: 2s
plot: [2,1] [=============>-------------------------------] 31% est: 3s
plot: [2,2] [================>----------------------------] 38% est: 3s
plot: [2,3] [===================>-------------------------] 44% est: 3s
plot: [2,4] [=====================>-----------------------] 50% est: 2s
plot: [3,1] [========================>--------------------] 56% est: 2s
plot: [3,2] [===========================>-----------------] 62% est: 2s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 1s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 2s
plot: [1,3] [=======>-------------------------------------] 19% est: 2s
plot: [1,4] [==========>----------------------------------] 25% est: 2s
plot: [2,1] [=============>-------------------------------] 31% est: 2s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 2s
plot: [1,4] [==========>----------------------------------] 25% est: 2s
plot: [2,1] [=============>-------------------------------] 31% est: 2s
plot: [2,2] [================>----------------------------] 38% est: 2s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 2s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 2s
plot: [2,2] [================>----------------------------] 38% est: 2s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 2s
plot: [1,4] [==========>----------------------------------] 25% est: 2s
plot: [2,1] [=============>-------------------------------] 31% est: 2s
plot: [2,2] [================>----------------------------] 38% est: 2s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 1s
plot: [1,4] [==========>----------------------------------] 25% est: 1s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 1s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
plot: [1,1] [==>------------------------------------------] 6% est: 0s
plot: [1,2] [=====>---------------------------------------] 12% est: 1s
plot: [1,3] [=======>-------------------------------------] 19% est: 2s
plot: [1,4] [==========>----------------------------------] 25% est: 2s
plot: [2,1] [=============>-------------------------------] 31% est: 1s
plot: [2,2] [================>----------------------------] 38% est: 1s
plot: [2,3] [===================>-------------------------] 44% est: 1s
plot: [2,4] [=====================>-----------------------] 50% est: 1s
plot: [3,1] [========================>--------------------] 56% est: 1s
plot: [3,2] [===========================>-----------------] 62% est: 1s
plot: [3,3] [==============================>--------------] 69% est: 1s
plot: [3,4] [=================================>-----------] 75% est: 1s
plot: [4,1] [====================================>--------] 81% est: 0s
plot: [4,2] [======================================>------] 88% est: 0s
plot: [4,3] [=========================================>---] 94% est: 0s
plot: [4,4] [=============================================]100% est: 0s
###Pearson Correlation Matrix
The following correlation matrix illustrates the correlation strength that exists between two taxa by the intensity of the color. A correlation illustrated by the white color indicates a correlation force that tends to a value of 0 and a correlation illustrated by the dark blue color indicates a correlation force that tends to a value of 1. Pearson correlation values between taxa are shown in the lower left portion of the figure. This matrix also illustrates the proportion of non-zero abundance values that a taxon has with respect to all the abundance values observed in the latter. Only taxa present in more than 25% of the visits are shown.
###Taxa Abundance through Time
It could be interesting to look at the abundance of taxa through time to see dynamics or abrupt changes.
Pour chaque taxon, the first graph (top left) shows the abundance value of the data by sampling years. The second graph (top right) illustrates the observed patterns of changes in abundance of individuals over time. The third graph (bottom left) illustrates future forecasts of changes in abundance of individuals over time. On this graph, the blue line corresponds to the expected average trend of changes over time, the dark gray zone corresponds to a confidence interval of 80% and the pale gray zone corresponds to a 95% confidence interval. The fourth graph (bottom right) illustrates time series autocorrelation (ACF). An autocorrelation value greater than the 95% confidence interval illustrated by the dotted line indicates a possible dependency between the variable and the time of year (time). For example, a certain value of abundance of a taxon observed in a given year could be explained by a certain event dating from a previous year (lag in time in years). It should be noted that the autocorrelation at offset time 0 is, by definition, equal to 1.
The following results present the calculation of the Box-Ljung statistical test applied to each taxon and are complementary to the previous graphs. A value of P (p-value) lower than 0.05 indicates that the residual values of a variable depend on the period of the year (time).
[1] "Ameletidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.052817, df = 1, p-value = 0.8182
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.13158, df = 2, p-value = 0.9363
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.61146, df = 3, p-value = 0.8938
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.85553, df = 4, p-value = 0.9309
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.4095, df = 5, p-value = 0.9233
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.8029, df = 6, p-value = 0.8332
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.0036, df = 7, p-value = 0.6595
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.0092, df = 8, p-value = 0.7566
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.0145, df = 9, p-value = 0.833
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.5174, df = 10, p-value = 0.854
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.5406, df = 11, p-value = 0.9022
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.5712, df = 12, p-value = 0.9361
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.7633, df = 13, p-value = 0.9543
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.7649, df = 14, p-value = 0.9721
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.1548, df = 15, p-value = 0.977
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4635, df = 16, p-value = 0.9633
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6062, df = 17, p-value = 0.9518
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.5394, df = 18, p-value = 0.9459
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.5465, df = 19, p-value = 0.9633
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.5541, df = 20, p-value = 0.9756
[1] "Ametropodidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Aturidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.022128, df = 1, p-value = 0.8817
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.38926, df = 2, p-value = 0.8231
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1651, df = 3, p-value = 0.7614
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4735, df = 4, p-value = 0.6494
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4836, df = 5, p-value = 0.779
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.572, df = 6, p-value = 0.5998
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5107, df = 7, p-value = 0.3777
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5229, df = 8, p-value = 0.4814
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5427, df = 9, p-value = 0.5808
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.385, df = 10, p-value = 0.5913
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.4789, df = 11, p-value = 0.6699
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.543, df = 12, p-value = 0.7414
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.7924, df = 13, p-value = 0.7884
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.876, df = 14, p-value = 0.8389
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7184, df = 15, p-value = 0.8371
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9141, df = 16, p-value = 0.8711
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9556, df = 17, p-value = 0.9055
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.039, df = 18, p-value = 0.9306
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.043, df = 19, p-value = 0.9519
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.87, df = 20, p-value = 0.9495
[1] "Baetidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.19447, df = 1, p-value = 0.6592
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.21106, df = 2, p-value = 0.8998
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.64621, df = 3, p-value = 0.8858
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.78266, df = 4, p-value = 0.9408
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.2051, df = 5, p-value = 0.8201
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.2734, df = 6, p-value = 0.8929
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.2485, df = 7, p-value = 0.7508
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.2582, df = 8, p-value = 0.8331
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.3481, df = 9, p-value = 0.887
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.8794, df = 10, p-value = 0.8253
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.9176, df = 11, p-value = 0.8788
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.0309, df = 12, p-value = 0.9145
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.9702, df = 13, p-value = 0.9037
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.0803, df = 14, p-value = 0.9316
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3964, df = 15, p-value = 0.9457
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3972, df = 16, p-value = 0.9648
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4098, df = 17, p-value = 0.9776
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4123, df = 18, p-value = 0.9862
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4347, df = 19, p-value = 0.9915
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.2579, df = 20, p-value = 0.99
[1] "Baetiscidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.0079245, df = 1, p-value = 0.9291
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.016029, df = 2, p-value = 0.992
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.024317, df = 3, p-value = 0.999
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.032793, df = 4, p-value = 0.9999
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.041461, df = 5, p-value = 1
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.050323, df = 6, p-value = 1
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.059385, df = 7, p-value = 1
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.06865, df = 8, p-value = 1
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.078123, df = 9, p-value = 1
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.087809, df = 10, p-value = 1
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.09771, df = 11, p-value = 1
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.10783, df = 12, p-value = 1
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.11818, df = 13, p-value = 1
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.12876, df = 14, p-value = 1
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.13957, df = 15, p-value = 1
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.15063, df = 16, p-value = 1
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.16193, df = 17, p-value = 1
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.17348, df = 18, p-value = 1
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.18528, df = 19, p-value = 1
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.19735, df = 20, p-value = 1
[1] "Brachycentridae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.029427, df = 1, p-value = 0.8638
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.29169, df = 2, p-value = 0.8643
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.4342, df = 3, p-value = 0.3294
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.431, df = 4, p-value = 0.03376
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.743, df = 5, p-value = 0.05672
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 12.516, df = 6, p-value = 0.0514
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.751, df = 7, p-value = 0.0558
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.829, df = 8, p-value = 0.08634
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.836, df = 9, p-value = 0.1283
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.987, df = 10, p-value = 0.1736
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.025, df = 11, p-value = 0.2316
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.26, df = 12, p-value = 0.2844
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.666, df = 13, p-value = 0.3286
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 15.232, df = 14, p-value = 0.3625
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.223, df = 15, p-value = 0.3674
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.228, df = 16, p-value = 0.4372
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.131, df = 17, p-value = 0.4455
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.195, df = 18, p-value = 0.5097
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 18.738, df = 19, p-value = 0.4738
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 19.515, df = 20, p-value = 0.4886
[1] "Capniidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Ceratopogonidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Chironomidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.11914, df = 1, p-value = 0.73
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.41039, df = 2, p-value = 0.8145
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.5016, df = 3, p-value = 0.9185
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.4434, df = 4, p-value = 0.8366
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.5183, df = 5, p-value = 0.9109
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.7044, df = 6, p-value = 0.8449
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.8895, df = 7, p-value = 0.5527
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.9004, df = 8, p-value = 0.6584
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.9101, df = 9, p-value = 0.7489
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.0961, df = 10, p-value = 0.7163
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.1979, df = 11, p-value = 0.7828
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2636, df = 12, p-value = 0.8397
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.7693, df = 13, p-value = 0.8583
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.924, df = 14, p-value = 0.8932
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.62, df = 15, p-value = 0.8965
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6964, df = 16, p-value = 0.9254
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.7957, df = 17, p-value = 0.9465
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.8141, df = 18, p-value = 0.9639
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.8416, df = 19, p-value = 0.976
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.6379, df = 20, p-value = 0.9743
[1] "Chloroperlidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.029549, df = 1, p-value = 0.8635
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.29133, df = 2, p-value = 0.8644
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.4295, df = 3, p-value = 0.33
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.437, df = 4, p-value = 0.03367
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.749, df = 5, p-value = 0.05659
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 12.514, df = 6, p-value = 0.05144
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.744, df = 7, p-value = 0.05593
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.822, df = 8, p-value = 0.08653
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.829, df = 9, p-value = 0.1285
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.979, df = 10, p-value = 0.1739
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.017, df = 11, p-value = 0.232
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.252, df = 12, p-value = 0.2849
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.658, df = 13, p-value = 0.3292
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 15.224, df = 14, p-value = 0.363
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.214, df = 15, p-value = 0.368
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.219, df = 16, p-value = 0.4378
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.12, df = 17, p-value = 0.4462
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.184, df = 18, p-value = 0.5105
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 18.731, df = 19, p-value = 0.4742
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 19.502, df = 20, p-value = 0.4894
[1] "Edwardsiidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Elmidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Empididae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.038038, df = 1, p-value = 0.8454
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.35283, df = 2, p-value = 0.8383
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.0833, df = 3, p-value = 0.7811
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.5001, df = 4, p-value = 0.6446
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.5071, df = 5, p-value = 0.7754
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6273, df = 6, p-value = 0.5924
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6855, df = 7, p-value = 0.3611
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6987, df = 8, p-value = 0.4634
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.7189, df = 9, p-value = 0.5627
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5345, df = 10, p-value = 0.5768
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6044, df = 11, p-value = 0.6584
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6696, df = 12, p-value = 0.7309
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9016, df = 13, p-value = 0.7803
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.0039, df = 14, p-value = 0.8308
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.8344, df = 15, p-value = 0.83
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.051, df = 16, p-value = 0.8639
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.091, df = 17, p-value = 0.8997
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.161, df = 18, p-value = 0.9265
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.169, df = 19, p-value = 0.9486
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 11.086, df = 20, p-value = 0.944
[1] "Enchytraeidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Ephemerellidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.0068903, df = 1, p-value = 0.9338
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.030535, df = 2, p-value = 0.9848
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.099753, df = 3, p-value = 0.9919
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.15729, df = 4, p-value = 0.9971
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.2822, df = 5, p-value = 0.9368
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.2829, df = 6, p-value = 0.9726
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.1078, df = 7, p-value = 0.7673
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.1078, df = 8, p-value = 0.8473
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.1079, df = 9, p-value = 0.9042
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6589, df = 10, p-value = 0.9128
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.7365, df = 11, p-value = 0.9433
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.8952, df = 12, p-value = 0.9213
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.0697, df = 13, p-value = 0.9436
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.2074, df = 14, p-value = 0.961
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4034, df = 15, p-value = 0.9455
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4067, df = 16, p-value = 0.9646
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5634, df = 17, p-value = 0.975
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.4326, df = 18, p-value = 0.9715
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.4439, df = 19, p-value = 0.9816
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.3911, df = 20, p-value = 0.9779
[1] "Ephemeridae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Feltriidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Glossosomatidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.044304, df = 1, p-value = 0.8333
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.32287, df = 2, p-value = 0.8509
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.0294, df = 3, p-value = 0.7941
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4512, df = 4, p-value = 0.6534
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4582, df = 5, p-value = 0.7828
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6163, df = 6, p-value = 0.5939
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6113, df = 7, p-value = 0.3681
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6192, df = 8, p-value = 0.4715
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6385, df = 9, p-value = 0.571
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5024, df = 10, p-value = 0.5799
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5881, df = 11, p-value = 0.6599
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6564, df = 12, p-value = 0.732
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9035, df = 13, p-value = 0.7802
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9721, df = 14, p-value = 0.8328
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.8229, df = 15, p-value = 0.8307
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.045, df = 16, p-value = 0.8643
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.095, df = 17, p-value = 0.8996
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.155, df = 18, p-value = 0.9267
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.165, df = 19, p-value = 0.9487
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 11.045, df = 20, p-value = 0.9451
[1] "Heptageniidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.028727, df = 1, p-value = 0.8654
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.29627, df = 2, p-value = 0.8623
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.35641, df = 3, p-value = 0.9491
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.36362, df = 4, p-value = 0.9853
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.0369, df = 5, p-value = 0.9595
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.4313, df = 6, p-value = 0.9639
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.2904, df = 7, p-value = 0.8569
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.3239, df = 8, p-value = 0.9124
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.3829, df = 9, p-value = 0.9472
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.7369, df = 10, p-value = 0.908
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.8984, df = 11, p-value = 0.936
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.975, df = 12, p-value = 0.9588
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.1139, df = 13, p-value = 0.9419
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.5817, df = 14, p-value = 0.9496
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.0196, df = 15, p-value = 0.9571
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.1133, df = 16, p-value = 0.971
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5472, df = 17, p-value = 0.9753
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5503, df = 18, p-value = 0.9846
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.0224, df = 19, p-value = 0.9864
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.7078, df = 20, p-value = 0.9861
[1] "Hydrophilidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Hydropsychidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.20186, df = 1, p-value = 0.6532
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.32611, df = 2, p-value = 0.8495
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.84646, df = 3, p-value = 0.8383
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.92601, df = 4, p-value = 0.9208
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.9274, df = 5, p-value = 0.7112
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.9275, df = 6, p-value = 0.8179
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6927, df = 7, p-value = 0.6974
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.7085, df = 8, p-value = 0.7882
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.8355, df = 9, p-value = 0.8484
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.3488, df = 10, p-value = 0.7852
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.3644, df = 11, p-value = 0.848
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.474, df = 12, p-value = 0.8903
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3171, df = 13, p-value = 0.8851
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3564, df = 14, p-value = 0.9201
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6396, df = 15, p-value = 0.9374
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6412, df = 16, p-value = 0.9588
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6413, df = 17, p-value = 0.9736
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6414, df = 18, p-value = 0.9835
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6416, df = 19, p-value = 0.9899
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.3936, df = 20, p-value = 0.9889
[1] "Hydroptilidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.021066, df = 1, p-value = 0.8846
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.3944, df = 2, p-value = 0.821
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1928, df = 3, p-value = 0.7547
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4701, df = 4, p-value = 0.65
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4801, df = 5, p-value = 0.7795
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6205, df = 6, p-value = 0.5933
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6282, df = 7, p-value = 0.3665
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6399, df = 8, p-value = 0.4694
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6605, df = 9, p-value = 0.5687
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5198, df = 10, p-value = 0.5782
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6127, df = 11, p-value = 0.6576
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6781, df = 12, p-value = 0.7301
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9329, df = 13, p-value = 0.778
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.0162, df = 14, p-value = 0.83
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.8681, df = 15, p-value = 0.828
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.059, df = 16, p-value = 0.8635
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.101, df = 17, p-value = 0.8993
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.182, df = 18, p-value = 0.9258
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.187, df = 19, p-value = 0.9482
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 11.002, df = 20, p-value = 0.9462
[1] "Hydrozetidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Hydryphantidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.021066, df = 1, p-value = 0.8846
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.3944, df = 2, p-value = 0.821
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1928, df = 3, p-value = 0.7547
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4701, df = 4, p-value = 0.65
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4801, df = 5, p-value = 0.7795
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6205, df = 6, p-value = 0.5933
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6282, df = 7, p-value = 0.3665
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6399, df = 8, p-value = 0.4694
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6605, df = 9, p-value = 0.5687
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5198, df = 10, p-value = 0.5782
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6127, df = 11, p-value = 0.6576
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6781, df = 12, p-value = 0.7301
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9329, df = 13, p-value = 0.778
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.0162, df = 14, p-value = 0.83
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.8681, df = 15, p-value = 0.828
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.059, df = 16, p-value = 0.8635
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.101, df = 17, p-value = 0.8993
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.182, df = 18, p-value = 0.9258
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.187, df = 19, p-value = 0.9482
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 11.002, df = 20, p-value = 0.9462
[1] "Hygrobatidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Lebertiidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.022128, df = 1, p-value = 0.8817
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.38926, df = 2, p-value = 0.8231
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1651, df = 3, p-value = 0.7614
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4735, df = 4, p-value = 0.6494
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4836, df = 5, p-value = 0.779
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.572, df = 6, p-value = 0.5998
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5107, df = 7, p-value = 0.3777
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5229, df = 8, p-value = 0.4814
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5427, df = 9, p-value = 0.5808
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.385, df = 10, p-value = 0.5913
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.4789, df = 11, p-value = 0.6699
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.543, df = 12, p-value = 0.7414
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.7924, df = 13, p-value = 0.7884
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.876, df = 14, p-value = 0.8389
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7184, df = 15, p-value = 0.8371
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9141, df = 16, p-value = 0.8711
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9556, df = 17, p-value = 0.9055
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.039, df = 18, p-value = 0.9306
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.043, df = 19, p-value = 0.9519
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.87, df = 20, p-value = 0.9495
[1] "Lepidostomatidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.013839, df = 1, p-value = 0.9064
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.19214, df = 2, p-value = 0.9084
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.40071, df = 3, p-value = 0.9401
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.40448, df = 4, p-value = 0.9821
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.61416, df = 5, p-value = 0.9873
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.93065, df = 6, p-value = 0.9881
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.2817, df = 7, p-value = 0.9889
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.2833, df = 8, p-value = 0.9957
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.1701, df = 9, p-value = 0.9885
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.8678, df = 10, p-value = 0.9531
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.9191, df = 11, p-value = 0.9722
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.9207, df = 12, p-value = 0.9848
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.1655, df = 13, p-value = 0.9399
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.4028, df = 14, p-value = 0.9553
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.5663, df = 15, p-value = 0.9686
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.9683, df = 16, p-value = 0.9739
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.7543, df = 17, p-value = 0.9715
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.9165, df = 18, p-value = 0.9799
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.1322, df = 19, p-value = 0.9712
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.2027, df = 20, p-value = 0.9804
[1] "Leptoceridae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.0079245, df = 1, p-value = 0.9291
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.016029, df = 2, p-value = 0.992
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.024317, df = 3, p-value = 0.999
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.032793, df = 4, p-value = 0.9999
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.041461, df = 5, p-value = 1
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.050323, df = 6, p-value = 1
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.059385, df = 7, p-value = 1
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.06865, df = 8, p-value = 1
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.078123, df = 9, p-value = 1
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.087809, df = 10, p-value = 1
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.09771, df = 11, p-value = 1
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.10783, df = 12, p-value = 1
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.11818, df = 13, p-value = 1
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.12876, df = 14, p-value = 1
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.13957, df = 15, p-value = 1
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.15063, df = 16, p-value = 1
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.16193, df = 17, p-value = 1
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.17348, df = 18, p-value = 1
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.18528, df = 19, p-value = 1
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.19735, df = 20, p-value = 1
[1] "Leptohyphidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.053379, df = 1, p-value = 0.8173
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.13285, df = 2, p-value = 0.9357
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.61754, df = 3, p-value = 0.8924
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.86631, df = 4, p-value = 0.9293
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.4227, df = 5, p-value = 0.9218
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.8272, df = 6, p-value = 0.8302
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.0399, df = 7, p-value = 0.6551
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.0454, df = 8, p-value = 0.7527
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.0507, df = 9, p-value = 0.8299
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.5553, df = 10, p-value = 0.8511
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.5783, df = 11, p-value = 0.9
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.6091, df = 12, p-value = 0.9345
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.8017, df = 13, p-value = 0.9531
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.8033, df = 14, p-value = 0.9712
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.194, df = 15, p-value = 0.9763
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5003, df = 16, p-value = 0.9624
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6424, df = 17, p-value = 0.9508
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.5791, df = 18, p-value = 0.9448
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.5858, df = 19, p-value = 0.9624
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.5925, df = 20, p-value = 0.975
[1] "Leptophlebiidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.047252, df = 1, p-value = 0.8279
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.2355, df = 2, p-value = 0.8889
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.2125, df = 3, p-value = 0.36
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.44, df = 4, p-value = 0.03364
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.784, df = 5, p-value = 0.05584
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 12.529, df = 6, p-value = 0.05116
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.779, df = 7, p-value = 0.05525
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.855, df = 8, p-value = 0.08564
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.86, df = 9, p-value = 0.1274
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.01, df = 10, p-value = 0.1726
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.045, df = 11, p-value = 0.2305
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.309, df = 12, p-value = 0.2814
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.755, df = 13, p-value = 0.3229
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 15.374, df = 14, p-value = 0.3531
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.305, df = 15, p-value = 0.3621
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.305, df = 16, p-value = 0.4319
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.182, df = 17, p-value = 0.4421
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.242, df = 18, p-value = 0.5065
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 18.89, df = 19, p-value = 0.4639
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 19.691, df = 20, p-value = 0.4774
[1] "Limnephilidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Lumbriculidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Naididae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.045393, df = 1, p-value = 0.8313
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.35534, df = 2, p-value = 0.8372
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.89288, df = 3, p-value = 0.8271
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.1038, df = 4, p-value = 0.7167
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.1225, df = 5, p-value = 0.8319
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.0533, df = 6, p-value = 0.6695
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.1359, df = 7, p-value = 0.4149
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.1516, df = 8, p-value = 0.5204
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.167, df = 9, p-value = 0.6197
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.1141, df = 10, p-value = 0.6177
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.1888, df = 11, p-value = 0.6963
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.2647, df = 12, p-value = 0.7641
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5442, df = 13, p-value = 0.8064
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6599, df = 14, p-value = 0.8522
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.428, df = 15, p-value = 0.8541
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.5925, df = 16, p-value = 0.887
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.6355, df = 17, p-value = 0.9182
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.698, df = 18, p-value = 0.9413
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7033, df = 19, p-value = 0.9599
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.543, df = 20, p-value = 0.9572
[1] "Nemouridae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.021066, df = 1, p-value = 0.8846
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.3944, df = 2, p-value = 0.821
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1928, df = 3, p-value = 0.7547
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4701, df = 4, p-value = 0.65
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4801, df = 5, p-value = 0.7795
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6205, df = 6, p-value = 0.5933
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6282, df = 7, p-value = 0.3665
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6399, df = 8, p-value = 0.4694
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6605, df = 9, p-value = 0.5687
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5198, df = 10, p-value = 0.5782
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6127, df = 11, p-value = 0.6576
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6781, df = 12, p-value = 0.7301
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9329, df = 13, p-value = 0.778
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.0162, df = 14, p-value = 0.83
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.8681, df = 15, p-value = 0.828
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.059, df = 16, p-value = 0.8635
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.101, df = 17, p-value = 0.8993
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.182, df = 18, p-value = 0.9258
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.187, df = 19, p-value = 0.9482
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 11.002, df = 20, p-value = 0.9462
[1] "Oreoleptidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.07013, df = 1, p-value = 0.7911
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.52795, df = 2, p-value = 0.768
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.133, df = 3, p-value = 0.7691
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.5678, df = 4, p-value = 0.6325
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.2587, df = 5, p-value = 0.5128
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2488, df = 6, p-value = 0.2985
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2556, df = 7, p-value = 0.4028
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2626, df = 8, p-value = 0.5086
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2698, df = 9, p-value = 0.6091
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2771, df = 10, p-value = 0.6991
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2845, df = 11, p-value = 0.7756
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.2922, df = 12, p-value = 0.8377
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3, df = 13, p-value = 0.886
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.308, df = 14, p-value = 0.9222
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3162, df = 15, p-value = 0.9483
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3245, df = 16, p-value = 0.9665
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.333, df = 17, p-value = 0.9788
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3418, df = 18, p-value = 0.9869
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3507, df = 19, p-value = 0.9921
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.3598, df = 20, p-value = 0.9953
[1] "Peltoperlidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.0079245, df = 1, p-value = 0.9291
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.016029, df = 2, p-value = 0.992
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.024317, df = 3, p-value = 0.999
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.032793, df = 4, p-value = 0.9999
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.041461, df = 5, p-value = 1
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.050323, df = 6, p-value = 1
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.059385, df = 7, p-value = 1
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.06865, df = 8, p-value = 1
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.078123, df = 9, p-value = 1
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.087809, df = 10, p-value = 1
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.09771, df = 11, p-value = 1
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.10783, df = 12, p-value = 1
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.11818, df = 13, p-value = 1
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.12876, df = 14, p-value = 1
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.13957, df = 15, p-value = 1
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.15063, df = 16, p-value = 1
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.16193, df = 17, p-value = 1
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.17348, df = 18, p-value = 1
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.18528, df = 19, p-value = 1
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.19735, df = 20, p-value = 1
[1] "Perlidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Perlodidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.013862, df = 1, p-value = 0.9063
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.0183, df = 2, p-value = 0.9909
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.044614, df = 3, p-value = 0.9975
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.062965, df = 4, p-value = 0.9995
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.16671, df = 5, p-value = 0.9994
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.90096, df = 6, p-value = 0.9891
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.093, df = 7, p-value = 0.9932
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1262, df = 8, p-value = 0.9973
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.222, df = 9, p-value = 0.9987
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.6424, df = 10, p-value = 0.962
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.7195, df = 11, p-value = 0.9774
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.7466, df = 12, p-value = 0.9876
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.6597, df = 13, p-value = 0.9188
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.8659, df = 14, p-value = 0.9398
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.9353, df = 15, p-value = 0.9594
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.033, df = 16, p-value = 0.9726
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.8269, df = 17, p-value = 0.9455
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.0272, df = 18, p-value = 0.9591
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7306, df = 19, p-value = 0.9593
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7322, df = 20, p-value = 0.9728
[1] "Phryganeidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Pionidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.021066, df = 1, p-value = 0.8846
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.3944, df = 2, p-value = 0.821
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1928, df = 3, p-value = 0.7547
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4701, df = 4, p-value = 0.65
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4801, df = 5, p-value = 0.7795
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.6205, df = 6, p-value = 0.5933
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6282, df = 7, p-value = 0.3665
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6399, df = 8, p-value = 0.4694
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6605, df = 9, p-value = 0.5687
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5198, df = 10, p-value = 0.5782
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6127, df = 11, p-value = 0.6576
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.6781, df = 12, p-value = 0.7301
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9329, df = 13, p-value = 0.778
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.0162, df = 14, p-value = 0.83
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.8681, df = 15, p-value = 0.828
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.059, df = 16, p-value = 0.8635
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.101, df = 17, p-value = 0.8993
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.182, df = 18, p-value = 0.9258
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.187, df = 19, p-value = 0.9482
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 11.002, df = 20, p-value = 0.9462
[1] "Planorbidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Poduridae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Polycentropodidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.055471, df = 1, p-value = 0.8138
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.13775, df = 2, p-value = 0.9334
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.6418, df = 3, p-value = 0.8868
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.90638, df = 4, p-value = 0.9236
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.47, df = 5, p-value = 0.9165
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.9118, df = 6, p-value = 0.8198
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.1705, df = 7, p-value = 0.6392
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.1755, df = 8, p-value = 0.7387
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.1809, df = 9, p-value = 0.8183
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.6932, df = 10, p-value = 0.8403
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.7152, df = 11, p-value = 0.8917
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.7472, df = 12, p-value = 0.9283
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.9426, df = 13, p-value = 0.9482
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.9444, df = 14, p-value = 0.9679
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.3415, df = 15, p-value = 0.9734
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.6388, df = 16, p-value = 0.9589
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.783, df = 17, p-value = 0.9468
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7305, df = 18, p-value = 0.9403
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7359, df = 19, p-value = 0.9592
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7406, df = 20, p-value = 0.9726
[1] "Psychodidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.029216, df = 1, p-value = 0.8643
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.29242, df = 2, p-value = 0.864
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 3.4425, df = 3, p-value = 0.3283
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.424, df = 4, p-value = 0.03386
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.736, df = 5, p-value = 0.05688
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 12.524, df = 6, p-value = 0.05125
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.762, df = 7, p-value = 0.05558
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.84, df = 8, p-value = 0.08604
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 13.846, df = 9, p-value = 0.1279
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.001, df = 10, p-value = 0.1729
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.039, df = 11, p-value = 0.2308
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.273, df = 12, p-value = 0.2836
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 14.681, df = 13, p-value = 0.3277
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 15.246, df = 14, p-value = 0.3616
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.237, df = 15, p-value = 0.3665
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 16.242, df = 16, p-value = 0.4362
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.149, df = 17, p-value = 0.4443
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 17.214, df = 18, p-value = 0.5085
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 18.749, df = 19, p-value = 0.4731
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 19.533, df = 20, p-value = 0.4875
[1] "Sialidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Simuliidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.022128, df = 1, p-value = 0.8817
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.38926, df = 2, p-value = 0.8231
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1651, df = 3, p-value = 0.7614
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4735, df = 4, p-value = 0.6494
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4836, df = 5, p-value = 0.779
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.572, df = 6, p-value = 0.5998
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5107, df = 7, p-value = 0.3777
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5229, df = 8, p-value = 0.4814
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5427, df = 9, p-value = 0.5808
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.385, df = 10, p-value = 0.5913
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.4789, df = 11, p-value = 0.6699
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.543, df = 12, p-value = 0.7414
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.7924, df = 13, p-value = 0.7884
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.876, df = 14, p-value = 0.8389
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7184, df = 15, p-value = 0.8371
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9141, df = 16, p-value = 0.8711
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9556, df = 17, p-value = 0.9055
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.039, df = 18, p-value = 0.9306
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.043, df = 19, p-value = 0.9519
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.87, df = 20, p-value = 0.9495
[1] "Sperchontidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.022187, df = 1, p-value = 0.8816
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.38895, df = 2, p-value = 0.8233
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1636, df = 3, p-value = 0.7617
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4737, df = 4, p-value = 0.6494
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4838, df = 5, p-value = 0.7789
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.5693, df = 6, p-value = 0.6001
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5041, df = 7, p-value = 0.3783
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5164, df = 8, p-value = 0.4821
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5361, df = 9, p-value = 0.5815
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.3776, df = 10, p-value = 0.592
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.4715, df = 11, p-value = 0.6705
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5356, df = 12, p-value = 0.742
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.7846, df = 13, p-value = 0.789
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.8683, df = 14, p-value = 0.8394
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7101, df = 15, p-value = 0.8376
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9061, df = 16, p-value = 0.8715
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9475, df = 17, p-value = 0.9058
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.032, df = 18, p-value = 0.9309
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.035, df = 19, p-value = 0.9521
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.863, df = 20, p-value = 0.9497
[1] "Taeniopterygidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA
[1] "Tipulidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.057226, df = 1, p-value = 0.8109
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.32206, df = 2, p-value = 0.8513
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.32854, df = 3, p-value = 0.9546
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.76857, df = 4, p-value = 0.9426
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.99066, df = 5, p-value = 0.9633
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.7522, df = 6, p-value = 0.941
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.5781, df = 7, p-value = 0.7113
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.5989, df = 8, p-value = 0.7995
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.5992, df = 9, p-value = 0.8678
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.8917, df = 10, p-value = 0.8243
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 5.9862, df = 11, p-value = 0.8743
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.0559, df = 12, p-value = 0.9132
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.6975, df = 13, p-value = 0.9171
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 6.8742, df = 14, p-value = 0.9395
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4125, df = 15, p-value = 0.9452
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4375, df = 16, p-value = 0.9639
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.4857, df = 17, p-value = 0.9763
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5092, df = 18, p-value = 0.9851
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5245, df = 19, p-value = 0.9908
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.3152, df = 20, p-value = 0.9896
[1] "Torrenticolidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.022604, df = 1, p-value = 0.8805
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 0.38443, df = 2, p-value = 0.8251
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 1.1599, df = 3, p-value = 0.7626
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4404, df = 4, p-value = 0.6553
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 2.4497, df = 5, p-value = 0.784
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 4.5629, df = 6, p-value = 0.601
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.551, df = 7, p-value = 0.3738
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5629, df = 8, p-value = 0.4773
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 7.5831, df = 9, p-value = 0.5766
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.4366, df = 10, p-value = 0.5863
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5281, df = 11, p-value = 0.6654
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.5931, df = 12, p-value = 0.7372
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.8454, df = 13, p-value = 0.7845
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 8.9267, df = 14, p-value = 0.8357
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.7738, df = 15, p-value = 0.8337
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 9.9671, df = 16, p-value = 0.8683
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.006, df = 17, p-value = 0.9034
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.086, df = 18, p-value = 0.9291
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.09, df = 19, p-value = 0.9507
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = 10.912, df = 20, p-value = 0.9485
[1] "Valvatidae"
[[1]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 1, p-value = NA
[[2]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 2, p-value = NA
[[3]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 3, p-value = NA
[[4]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 4, p-value = NA
[[5]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 5, p-value = NA
[[6]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 6, p-value = NA
[[7]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 7, p-value = NA
[[8]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 8, p-value = NA
[[9]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 9, p-value = NA
[[10]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 10, p-value = NA
[[11]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 11, p-value = NA
[[12]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 12, p-value = NA
[[13]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 13, p-value = NA
[[14]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 14, p-value = NA
[[15]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 15, p-value = NA
[[16]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 16, p-value = NA
[[17]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 17, p-value = NA
[[18]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 18, p-value = NA
[[19]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 19, p-value = NA
[[20]]
Box-Ljung test
data: resid(auto.arima)
X-squared = NaN, df = 20, p-value = NA###Spatial Correlation
The following results present the spatial autocorrelation results applied to each variable. A value of P (p-value) less than 0.05 makes it possible to assume that the spatial distribution of the values is subject to non-random spatial aggregation. When the value of P (p-value) is less than 0.05, a positive Moran index (Moran’s I) indicates that the values are aggregated with each other while a negative Moran index indicates that the values are scattered between them.
Warning in nb2listw(lw$neighbours, glist = lw$weights, style = "W",
zero.policy = T): zero sum general weights[1] "Ameletidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 39.315, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.7992764889 -0.0022624434 0.0004156638
[1] "Ametropodidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.16596, p-value = 0.8682
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0001278787 -0.0022624434 0.0001654212
[1] "Aturidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.644, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9102098435 -0.0022624434 0.0004177451
[1] "Baetidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 41.714, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8504246819 -0.0022624434 0.0004178435
[1] "Baetiscidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = -0.29697, p-value = 0.7665
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0060819580 -0.0022624434 0.0001654212
[1] "Brachycentridae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 41.56, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8472160298 -0.0022624434 0.0004177846
[1] "Capniidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 9.0284, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.1384999363 -0.0022624434 0.0002430805
[1] "Ceratopogonidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 2.1652, p-value = 0.03038
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.031039496 -0.002262443 0.000236569
[1] "Chironomidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 43.676, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.890336903 -0.002262443 0.000417660
[1] "Chloroperlidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 41.758, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8512705691 -0.0022624434 0.0004177891
[1] "Edwardsiidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.3479, p-value = 0.7279
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0022121684 -0.0022624434 0.0001654212
[1] "Elmidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.3479, p-value = 0.7279
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0022121684 -0.0022624434 0.0001654212
[1] "Empididae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.572, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9087386413 -0.0022624434 0.0004177456
[1] "Enchytraeidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.14209, p-value = 0.887
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0004349350 -0.0022624434 0.0001654212
[1] "Ephemerellidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 36.563, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.7450559213 -0.0022624434 0.0004177552
[1] "Ephemeridae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.12504, p-value = 0.9005
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0004698265 -0.0022624434 0.0002055415
[1] "Feltriidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.14209, p-value = 0.887
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0004349350 -0.0022624434 0.0001654212
[1] "Glossosomatidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.695, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9112710979 -0.0022624434 0.0004177662
[1] "Heptageniidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 38.073, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.7758494304 -0.0022624434 0.0004176826
[1] "Hydrophilidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.14234, p-value = 0.8868
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0004317576 -0.0022624434 0.0001654212
[1] "Hydropsychidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 41.029, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8364611730 -0.0022624434 0.0004178886
[1] "Hydroptilidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.892, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9152767101 -0.0022624434 0.0004177451
[1] "Hydrozetidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.14234, p-value = 0.8868
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0004317576 -0.0022624434 0.0001654212
[1] "Hydryphantidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 42.631, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8688960397 -0.0022624434 0.0004175777
[1] "Hygrobatidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.14209, p-value = 0.887
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0004349350 -0.0022624434 0.0001654212
[1] "Lebertiidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.66, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9105320729 -0.0022624434 0.0004177453
[1] "Lepidostomatidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 40.412, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8237738009 -0.0022624434 0.0004178072
[1] "Leptoceridae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.15258, p-value = 0.8787
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0002999705 -0.0022624434 0.0001654212
[1] "Leptohyphidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 41.582, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8458594699 -0.0022624434 0.0004160023
[1] "Leptophlebiidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 39.969, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8146924685 -0.0022624434 0.0004177719
[1] "Limnephilidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 4.4436, p-value = 8.845e-06
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0571496854 -0.0022624434 0.0001787607
[1] "Lumbriculidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 4.7796, p-value = 1.757e-06
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0620641894 -0.0022624434 0.0001811364
[1] "Naididae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.474, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9067478064 -0.0022624434 0.0004177555
[1] "Nemouridae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 12.291, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.2222610133 -0.0022624434 0.0003337159
[1] "Oreoleptidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.11163, p-value = 0.9111
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0003527087 -0.0022624434 0.0002926568
[1] "Peltoperlidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.14902, p-value = 0.8815
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0002884215 -0.0022624434 0.0002930126
[1] "Perlidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.15258, p-value = 0.8787
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0002999705 -0.0022624434 0.0001654212
[1] "Perlodidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 36.738, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.7475336276 -0.0022624434 0.0004165475
[1] "Phryganeidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.3479, p-value = 0.7279
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0022121684 -0.0022624434 0.0001654212
[1] "Pionidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.642, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9101695566 -0.0022624434 0.0004177449
[1] "Planorbidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.3479, p-value = 0.7279
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0022121684 -0.0022624434 0.0001654212
[1] "Poduridae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 5.1671, p-value = 2.377e-07
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0678631259 -0.0022624434 0.0001841863
[1] "Polycentropodidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 41.496, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8440995145 -0.0022624434 0.0004160087
[1] "Psychodidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 39.043, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.7957454761 -0.0022624434 0.0004177575
[1] "Sialidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.3479, p-value = 0.7279
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0022121684 -0.0022624434 0.0001654212
[1] "Simuliidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 43.13, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.879031290 -0.002262443 0.000417518
[1] "Sperchontidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.642, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9101667211 -0.0022624434 0.0004177451
[1] "Taeniopterygidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.14209, p-value = 0.887
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
-0.0004349350 -0.0022624434 0.0001654212
[1] "Tipulidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 43.667, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.8902676435 -0.0022624434 0.0004177665
[1] "Torrenticolidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 44.653, p-value < 2.2e-16
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.9103630342 -0.0022624434 0.0004177265
[1] "Valvatidae"
Moran I test under randomisation
data: dataset.BIO2[, j]
weights: lwW n reduced by no-neighbour observations
Moran I statistic standard deviate = 0.3479, p-value = 0.7279
alternative hypothesis: two.sided
sample estimates:
Moran I statistic Expectation Variance
0.0022121684 -0.0022624434 0.0001654212 ##Diversity
###Diversity Metrics
The following table presents the calculation of several diversity metrics per visit.
###Graphs for diversity
##Releases Notes What’s New, Updated, or Fixed in This Release
New 
Updated 
Fixed
CABIN_vv_biology.Rmd Version 1.1 — February 14, 2018
Update — Reduce the numbers of procedures and change their organisation.
CABIN_vv_biology.Rmd Version 1.0 — August 18, 2017
Première version.
Developped by Martin Jean and Evelyne Paquette-Boisclair
---
date: '2018-02-14'
output:
  html_notebook:
    df_print: paged
    number_sections: yes
    toc: yes
    toc_depth: 4
    toc_float: yes
  html_document:
    df_print: paged
    toc: yes
  word_document:
    toc: yes
version: '1.1'
---

| ![](../../../Configuration/gc_en.png)  |  ![](../../../Configuration/rcba_logo.png) |
|:------------------------------------|--------------------------------------:|                            


#CABIN VERIFICATION AND VALIDATION - BIOLOGICAL DATA

*Environment and Climate Change Canada*

*Analysis performed on `r Sys.time()`*

***

This report presents the results for verification and validation of a biological CABIN data file associated to the project **`r paste(datasetName)`**.

In this analysis, the biological dataset is checked to answer the following question:
  
  + Are the biological data reflecting the sites visited?

This document is a R notebook written in [R Markdown](http://rmarkdown.rstudio.com). When you execute the code embedded in the notebook, the results will appear under the corresponding code. To do this, place your cursor inside a chunk (box which contains *R* code) and click the green arrow to the right of it named *Run Current Chunk* or press *Ctrl+Shift+Enter* (*Cmd+Shift+Enter* in *macOS*) on your keyboard. Repeat for each chunk. As the code contained in this notebook is executed, the results will appear under each of the corresponding chunks in this window. Once all commands are executed, click the *Preview* button at the top left of this window or press the *Ctrl+Shift+K* keys (*Cmd+Shift+K* in *macOS*). A new window will appear and will contain the report of these verification and validation results for the biological CABIN data.



##Requirements

```{r 1, include=FALSE}
## Required Packages and Custom Functions
source("../../../Required_packages.R")
source("../../../Required_functions.R")


## Project Preferences
source("../../../Configuration/project_settings.R")

```

##Descriptive Statistics

The data file contains `r {nrow(dataset.BIO)}` visits (lines) and `r {ncol(dataset.BIO)}` variables (columns).

The following table presents a subset of the data.


**Reading Biological Data**

`r datatable(dataset.BIO, filter = "top", rownames=F, options = list(scrollX = TRUE )) `

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
>
> - *Does the file seem to been read correctly?*
> - *Are columns missing?*

**List of visits present in the biological data (ID from the CABIN database):**  

`r rownames(dataset.BIO) `

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
> - *Compare the list above with the following table which shows the visits from all the data for this project.*


`r datatable(dataset.NAM, filter = "top", rownames=F, options = list(scrollX = TRUE ))`

**Variables List:**  

`r colnames(dataset.BIO) `

> ![ACTION:](../../../Configuration/action.png)
> *Examine the variables above and make sure they are all present.


##General Statistics

The following table shows the main general parameters per variable. When the result on the *BinaryData* line of the array is *TRUE*, this indicates that the variable has binary data. However, this result can also be obtained because the variable has only one to two values at most. The *Na.values* line indicates the number of missing values for each variable.

**Table of General Statistics**

```{r 2, echo=FALSE}

#Table Of General Statistics

{summary.BIO <- as.data.frame(t(do.call(cbind, lapply(dataset.BIO, summary))))
summary.BIO$Std.deviation <- apply(dataset.BIO, 2, sd, na.rm = T)
summary.BIO$Length <- colSums(!is.na(dataset.BIO))
summary.BIO$BinaryData <- sapply(dataset.BIO,function(x)length(unique(na.omit(x)))<=2)
summary.BIO$NA.values <- colSums(is.na(dataset.BIO))
summary.BIO$`NA's` <- NULL
summary.BIO <- as.data.frame(t(summary.BIO))
summary.BIO
}
datatable(summary.BIO, filter = "top", rownames=F, options = list(scrollX = TRUE ))

```

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
> - *Examine the data and make sure it reflects reality. Examine statistics and identify, if present, anomalies with statistics.*


##Geographical Distribution


###Taxa Distribution

The following graphs show the location (latitude and longitude) ainsi que l'abondance en taxons des sites observées dans le fichier de données pour chaque taxon.

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
> - *Is the geographical distribution of taxa showing anomalies?*
> - *Is there data that are odd?*


```{r 3, echo=FALSE, warning=FALSE}
#Geographical distribution of taxa

dataset.GEN2 <- dataset.GEN
dataset.BIO2 <- left_join(dataset.BIO2, dataset.GEN2[,c("SampleId", "Longitude", "Latitude")], by = "SampleId")
if("Longitude.x" %in% colnames(dataset.BIO2)){colnames(dataset.BIO2)[which(colnames(dataset.BIO2) == "Longitude.x")] <- "Longitude"}
if("Latitude.x" %in% colnames(dataset.BIO2)){colnames(dataset.BIO2)[which(colnames(dataset.BIO2) == "Latitude.x")] <- "Latitude"}
if("Longitude.y" %in% colnames(dataset.BIO2)){dataset.BIO2$Longitude.y <- NULL}
if("Latitude.y" %in% colnames(dataset.BIO2)){dataset.BIO2$Latitude.y <- NULL}

size <- dataset.BIO2[(2:ncol(dataset.BIO2))-2]

for(i in 2:(ncol(dataset.BIO2)-2)){
graph.geo.ab <- ggplot(dataset.BIO2, aes(dataset.BIO2$Longitude, dataset.BIO2$Latitude)) +
  geom_point(shape = 21, aes(size = size[,i])) + #Broken on ggplot2 3.2.0, 3.1.0, 3.0.0
  ggtitle(paste("Distribution of", colnames(dataset.BIO2[i]))) +
  theme(plot.title = element_text(hjust = 0.5)) +
  scale_size(range = c(1,10), name = "Abundance")
graph.geo.ab
print(graph.geo.ab)
}

rm(size)
rm(dataset.GEN2)

```


###Richness Distribution

The following graphs show the location (latitude and longitude) and taxa richness on sites.

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
> 
> - *Is the geographical distribution of richness showing anomalies?*
> - *Are some richness measures out of the ordinary?*
> - *Is there a pattern in taxa richness?*


```{r 4, echo=FALSE}
#Geographica Distribution of taxa richness

dataset.BIO2$richness <- apply(dataset.BIO > 0, 1, sum, na.rm=TRUE)
dataset.SPA2 <- dataset.SPA
dataset.SPA2 <- left_join(dataset.SPA2, dataset.BIO2[,c("SampleId", "richness")], by = "SampleId")

if("richness.x" %in% colnames(dataset.SPA2)){colnames(dataset.SPA2)[which(colnames(dataset.SPA2) == "richness.x")] <- "richness"}
if("richness.y" %in% colnames(dataset.SPA2)){dataset.SPA2$richness.y <- NULL}

graph.geo.sp <- ggplot(dataset.SPA2, aes(dataset.SPA2$Longitude, dataset.SPA2$Latitude)) +
  geom_point(shape = 21, aes(size = dataset.SPA2$richness), na.rm = TRUE) +
  ggtitle(paste("Geographical Distribution of Taxa Richness")) +
  theme(plot.title = element_text(hjust = 0.5)) +
  scale_size(range = c(1,10), name = "Taxa Richness")
graph.geo.sp

```


The following interactive map illustrates the same data as the above graph. Click on a point to show the taxa richness.

```{r 5, echo=FALSE, fig.height=5.5, fig.width=8.5, message=FALSE, include=TRUE}
#Map of taxa richness

dataset.NAM2 <- dataset.NAM
dataset.NAM2$Site_Date_Number <- paste(dataset.NAM2$Site, dataset.NAM2$SampleDate, dataset.NAM2$SampleId)
dataset.SPA2 <- dataset.SPA
dataset.SPA2 <- left_join(dataset.SPA2, dataset.NAM2[,c("SampleId", "Site_Date_Number")], by = "SampleId")
dataset.SPA2 <- left_join(dataset.SPA2, dataset.NAM2[, c("SampleId", "Site", "SampleDate")], by = "SampleId")
dataset.SPA2 <- left_join(dataset.SPA2, dataset.BIO2[, c("SampleId", "richness")], by = "SampleId")
dataset.SPA2$Site_Date_Number <- paste(dataset.SPA2$Site, "_", dataset.SPA2$SampleDate, "_", dataset.SPA2$SampleId)
dates <- as.Date(dataset.SPA2$SampleDate, "%Y-%m-%d")
dataset.SPA2$Year <- strftime(parse_date_time(as.character(dates), "%Y-%m-%d"), "%y")
dataset.SPA2$Site_Year <- paste(dataset.SPA2$Site, "_", dataset.SPA2$Year)

cabinPoints <- SpatialPointsDataFrame(coords = na.omit(dataset.SPA[, 1:2]), data = na.omit(dataset.SPA[,1:2]))

getColor2 <- function(dataset.SPA2) {
  sapply(dataset.SPA2$SampleDate, function(SampleDate) {
  if(SampleDate <= "2005-12-31") {
    "green"
  } else if(SampleDate <= "2006-12-31") {
    "yellow"
  } else if(SampleDate <= "2007-12-31") {
    "darkorange"
  } else if(SampleDate <= "2008-12-31") {
    "red"
  } else if(SampleDate <= "2009-12-31") {
    "pink"
  } else if(SampleDate <= "2010-12-31") {
    "blue"
  } else if(SampleDate <= "2011-12-31") {
    "darkgreen"
  } else if(SampleDate <= "2012-12-31") {
    "cyan"
  } else if(SampleDate <= "2013-12-31") {
    "deeppink"
  } else if(SampleDate <= "2014-12-31") {
    "lightblue"
  } else if(SampleDate <= "2015-12-31") {
    "lightseagreen"
  } else if(SampleDate <= "2016-12-31") {
    "darkviolet"
  } else if(SampleDate <= "2017-12-31") {
    "darkred"
  } else if(SampleDate <= "2018-12-31") {
    "cadetblue"
  } else if(SampleDate <= "2019-12-31") {
    "black"
  } else if(SampleDate <= "2020-12-31") {
    "gray"
  } else {
    "brown"
  } })
} 

#dates <- as.Date(dataset.SPA2$SampleDate, "%Y-%m-%d") 
#dataset.SPA2$Year <- strftime(parse_date_time(as.character(dates), "%Y-%m-%d"), "%Y")
  
    leaflet(data = dataset.SPA2) %>% addProviderTiles(providers$Esri.WorldTopoMap) %>% addCircleMarkers(~Longitude, ~Latitude, popup = paste( "Site_Year:", dataset.SPA2$Site_Year, "<br>", "Richness: ", dataset.SPA2$richness, "<br>"), label = (dataset.SPA2$Site_Year), radius = dataset.SPA2$richness, color = getColor2(dataset.SPA2), stroke = F, fillOpacity = 0.5, clusterOptions = markerClusterOptions()) %>% addLegend(
  position = 'bottomright',
  colors = palette(),
  labels = palette(),
  opacity = 1,
  title = 'Legend')
  
rm(dataset.SPA2)

```


##Presence of Outliers

###Dispersion of Observed Values

The following scatterplots present the value of the taxa abundances observed in the dataset. Below the x-axis is the visit identifier for the corresponding observed value, namely the name of the site from which the data originated, its sampling date and the sampling number.

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
> 
> - *Is the distribution of abundance values indicating an ecological phenomenon or a potential problem (disturbance)?*
> - *Are some taxa very rare or very frequent?*
> - *Is a problem with abundant data apparent?*


```{r 6, echo=FALSE, warning=FALSE}
#Scatterplots of observed values


dataset.BIO2 <- left_join(dataset.BIO2, dataset.NAM2[,c("SampleId", "Site_Date_Number")], by = "SampleId")

for(j in 2:(ncol(dataset.BIO2)-4)){
plot.ab <- ggplot(dataset.BIO2, aes(x = dataset.BIO2$Site_Date_Number, y = dataset.BIO2[,j])) +
  geom_point() +
  labs(y = "Abondunce", x = "Visit") +
  theme(axis.text.x = element_text(angle = 90, vjust = -0.05)) +
   scale_x_discrete(breaks = "", labels = "", na.value = NA) +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle(paste("Abundance of", colnames(dataset.BIO2[j]), "per visit"))
plot.ab
print(plot.ab)
}

```

###Boxplots of Observed and Transformed Values

The first boxplot (left) shows the distribution of continuous variable values observed in the data file, the middle illustrates the distribution of the logarithmic-transformed values, and the third box plot (right) shows the distribution of the square-root-transformed values.

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following, putting your attention to points with a label (if present):*
> 
> - *Do data seem out of the ordinary?*
> - *Is a data transformation showing a better statistical distribution than raw data?*



```{r 7, echo=FALSE, message=FALSE, warning=FALSE}
#Boxplots for observed and transformed values

for(i in 1:ncol(dataset.BIO)){
box.ab.1 <- qplot(y = dataset.BIO[,i], x = "", geom = "boxplot", ylab = "Abundance") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Raw Data")
box.ab.2 <- qplot(y = dataset.BIO[,i], x = "", geom = "boxplot", ylab = "Log(Abundance)", log = "y") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Log")
box.ab.3 <- qplot(y = sqrt(dataset.BIO[,i]), x = "", geom = "boxplot", ylab = "Sqrt(Abundance)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Sqrt")

grid.arrange(box.ab.1, box.ab.2, box.ab.3, ncol = 3, top = colnames(dataset.BIO[i]))
}

```


###Identification of potential outliers

The first boxplot (left) shows the distribution of abundance values observed in the dataset and the dispersion diagram (right) show the distribution of abundance values in the order in which they appear in this file. Data with identification by their identifier on the diagrams are potentially outliers contained in the dataset. The method used to identify potentially outliers is the interquantile range (*IQR*). The IQR is calculated as follows:

`IQR(x) = quantile(x, 3/4) - quantile(x, 1/4)`


Potential outliers are defined as values below *Q1 - 1,5 IQR* or above *Q3 + 1,5 IQR*.

The identifier of the visit corresponds to the name of the site from which the data originated, its sampling date and the sample number for this visit.

The interpretation of Cleveland dotplot is done by looking at points that stick out on the right-hand side, or on the left-hand side. They are observed values that are considerable larger, or smaller, than the majority of the observations, and require further investigation. When the most likely explanation is that the extreme observations are measurement errors, they should be dropped because their presence is likely to dominate the analysis. On the other hand, if the removal of these values are not an option, a data transformation should be considered.

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
> 
> - *Are extreme values on the boxplots could be considered as outliers?*
> - *Are those same points on Cleveland dotplot isolated (on the left or right)?*
> - *Should some taxa (too rare or too abundant) not be considered in further analysis?*


```{r 8, echo=FALSE, warning=FALSE}
#Identification of potential outliers

for (j in 2:(ncol(dataset.BIO2)-4)) {
  boxplot <- ggplot(data = dataset.BIO2, aes(x = "", y = dataset.BIO2[,j])) +
    geom_boxplot() +
    ylab("Abundance") +
    xlab("") +
    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("Boxplot") +
    geom_text(aes(label = ifelse(dataset.BIO2[,j] < quantile(dataset.BIO2[,j], probs=c(.25), na.rm = T) - 1.5*IQR(dataset.BIO2[,j], na.rm = T)|dataset.BIO2[,j] > quantile(dataset.BIO2[,j], probs=c(.75), na.rm = T) + 1.5*IQR(dataset.BIO2[,j], na.rm = T), dataset.BIO2$Site_Date_Number, "")), hjust = -0.05, size = 3)

  dotchart <- ggplot(data = dataset.BIO2, aes(y = seq(dataset.BIO2[,j]), x = dataset.BIO2[,j], na.rm = T)) +
    geom_point(na.rm = T) +
    ylab("Order of the data") +
    xlab("Abundance") +
    theme(plot.title = element_text(hjust = 0.5)) +
    ggtitle("Cleveland Dotplot") +
    geom_text(aes(label = ifelse(dataset.BIO2[,j] < quantile(dataset.BIO2[,j], probs=c(.25), na.rm = T) - 1.5*IQR(dataset.BIO2[,j], na.rm = T)|dataset.BIO2[,j] > quantile(dataset.BIO2[,j], probs=c(.75), na.rm = T) + 1.5*IQR(dataset.BIO2[,j], na.rm = T), dataset.BIO2$Site_Date_Number, "")), hjust = -0.05, nudge_y = 2, size = 3)
        
  grid.arrange(boxplot, dotchart, ncol = 2, top = colnames(dataset.BIO2[j]))
}
```

The following list shows another way of identifying potential outliers contained in the dataset for each variable. When the identifier of a data item is indicated therein, this indicates that this data represents potentially aberrant data contained in the data file. *""* indicates non-aberrant data and *NA* indicates missing values. The identifier of the visit corresponds to the name of the site from which the data originated, its sampling date and the sampling number.

```{r 9, echo=FALSE, warning=FALSE}
#Identification of potential outliers (part 2)

for (j in 2:(ncol(dataset.BIO2)-4)) {
cat(colnames(dataset.BIO2[j]), "\n")
#list <- list(ifelse(dataset.BIO2[,j] < quantile(dataset.BIO2[,j], probs=c(.25), na.rm = T) - 1.5*IQR(dataset.BIO2[,j], na.rm = T) | dataset.BIO2[,j] > quantile(dataset.BIO2[,j], probs=c(.75), na.rm = T) + 1.5*IQR(dataset.BIO2[,j], na.rm = T), dataset.BIO2$Site_Date_Number, ""))
Active_Frame_Bio <- data.frame(ifelse(is.na(dataset.BIO2[,j]), "", ifelse(dataset.BIO2[,j] < quantile(dataset.BIO2[,j], probs=c(.25), na.rm = T) - 1.5*IQR(dataset.BIO2[,j], na.rm = T) | dataset.BIO2[,j] > quantile(dataset.BIO2[,j], probs=c(.75), na.rm = T) + 1.5*IQR(dataset.BIO2[,j], na.rm = T), dataset.BIO2$Site_Date_Number, "")))
Active_Frame_Bio["Is NA"] = list(ifelse(is.na(dataset.BIO2[,j]), "Yes", "No"))
colnames(Active_Frame_Bio) <- c("Site, Date and Number", "Is NA")
write.table(Active_Frame_Bio[Active_Frame_Bio$"Site, Date and Number" != "",], row.names = FALSE, quote = FALSE, na = "NA", sep = ", ")
cat("Number of Potential Outliers:", length(Active_Frame_Bio[Active_Frame_Bio$"Site, Date and Number" != "",][[1]]), "\n\n")
}

```

```{r 10, echo=FALSE, warning=FALSE}
#Identification of potential outliers (part 2)

for (j in 2:(ncol(dataset.BIO2)-4)) {
cat(colnames(dataset.BIO2[j]), "\n")
#list <- list(ifelse(dataset.BIO2[,j] < quantile(dataset.BIO2[,j], probs=c(.25), na.rm = T) - 1.5*IQR(dataset.BIO2[,j], na.rm = T) | dataset.BIO2[,j] > quantile(dataset.BIO2[,j], probs=c(.75), na.rm = T) + 1.5*IQR(dataset.BIO2[,j], na.rm = T), dataset.BIO2$Site_Date_Number, ""))
Active_Frame_Bio <- data.frame(ifelse(is.na(dataset.BIO2[,j]), dataset.BIO2$Site_Date_Number, ""))
Active_Frame_Bio["Is NA"] = list(ifelse(is.na(dataset.BIO2[,j]), "Yes", "No"))
colnames(Active_Frame_Bio) <- c("Site, Date and Number", "Is NA")
write.table(Active_Frame_Bio[Active_Frame_Bio$"Site, Date and Number" != "",], row.names = FALSE, quote = FALSE, na = "NA", sep = ", ")
cat("Number of Missing Values:", length(Active_Frame_Bio[Active_Frame_Bio$"Site, Date and Number" != "",][[1]]), "\n\n")
}

```

##Presence of zeros 

In ecological studies, we need to consider what it means when two species are jointly absent. This could say something important about the ecological characteristics of a site. When two sites both have the same joint absences, this might mean that the sites are ecologically similar. On the other hand, if a species has a highly clumped distribution, or is simply rare, then joint absences might arise through chance and say nothing about the suitability of a given site for a species, the similarity among the habitat needs of species or the ecological similarity of sites. A high frequency of zeros, thus, can greatly complicate interpretation of such analyses.

###Abundance Frequency Distribution

Abundance values a taxa commonly show a skew distribution that looks like a log-normal distribution, with many small to moderate values and a few extremely large values.

The following histogram illustrates the abundance frequency distribution of taxa. This chart helps, among other things, to evaluate the number of 0 values in the dataset. 

```{r 11, echo=FALSE, warning=FALSE}
#Histogram of Abundance Frequency distribution

#dataset.BIO_bk <- dataset.BIO
#dataset.BIO[is.na(dataset.BIO)] <- 0

count.mean <- mean(unlist(dataset.BIO))
count.sd <- sd(unlist(dataset.BIO))

count.n <- length(which(!is.na(unlist(dataset.BIO))))

count.bin <- ceiling((max(unlist(dataset.BIO))- min(unlist(dataset.BIO)))/nclass.Sturges(unlist(dataset.BIO)))

graph.count <- ggplot() +
  aes(round(unlist(dataset.BIO))) +
  geom_histogram(binwidth = count.bin, fill = I('lightblue'), color=I('black')) +
  xlab("Observed values") +
  ylab("Frequency") + 
  ggtitle("Abundance Frequencies") +
  theme(plot.title = element_text(hjust = 0.5))

ggiraph(code = print(graph.count), zoom_max = 10, height_svg = 4)

#dataset.BIO[dataset.BIO == 0] <- NA
#dataset.BIO <- dataset.BIO_bk

```


###Presence of double-zeros

We need to know whether there are double zeros in the data. This means that for each species-pair, we need to calculate how often both had zero abundance for the same site.

The following correlation matrix (or *corrgram*) illustrates the frequency with which pairs of taza both have zero abundance.The color and the amount that a circle has been filled correspond to the proportion of observations with double zeros. A white color indicates a correlation close to 0.5 whereas a dark blue color indicates a correlation near 1. This matrix also illustrates the proportion of correlation values. 0 abundances of a taxon relative to all abundance values observed in the latter. The diagonal running from bottom left to top right represents the percentage of observations of a taxa equal to zero. Only taxa present in more than 25% of the visits are shown.

> ![ACTION:](../../../Configuration/action.png)
> *Examine the following:*
> 
> 
> - *Are data showing a short or a long biological gradient?*
> - *What the presence of double zeros means for the data?*


```{r 12, echo=FALSE, fig.height=8, fig.width=14}
#Correlation matrix for double-zeros between taxa with a frequency > 25%

dataset.BIO[is.na(dataset.BIO)] <- 0

#Liste des taxons
AllS <- names(dataset.BIO)

#Détermine la richesse
occurrence <- colSums(dataset.BIO[,AllS] > 0, na.rm = TRUE)

#Delete all covariables
Benthos <- dataset.BIO[,AllS]

#Reducing the number of taxa represented in the figure, only those with a frequency > 25% are kept.
FreqMin <- 25
Benthos25 <- Benthos[, occurrence > (ncol(Benthos)/100*FreqMin)]
rm(Benthos)
N <- ncol(Benthos25)


AllNames <- names(Benthos25)
A <- matrix(nrow = N, ncol = N)

for (i in 1:N){
  for (j in 1:N){
    A[i,j] <- sum(dataset.BIO[,AllS[i]]==0  & dataset.BIO[,AllS[j]]==0, na.rm=TRUE)
  }
}


A1 <- A/nrow(Benthos25)
#print(A1, digits = 2)
rownames(A1) <- AllNames
colnames(A1) <- AllNames

panel.corrgram.2 <- function(x, y, z, subscripts, at = pretty(z), scale = 0.8, ...)
{
    require("grid", quietly = TRUE)
    x <- as.numeric(x)[subscripts]
    y <- as.numeric(y)[subscripts]
    z <- as.numeric(z)[subscripts]
    zcol <- level.colors(z, at = at, ...)
    for (i in seq(along = z))
    {
        lims <- range(0, z[i])
        tval <- 2 * base::pi *
            seq(from = lims[1], to = lims[2], by = 0.01)
        grid.circle(x = x[i], y = y[i], r = .5 * scale,
                    default.units = "native")
        grid.polygon(x = x[i] + .5 * scale * c(0, sin(tval)),
                     y = y[i] + .5 * scale * c(0, cos(tval)),
                     default.units = "native",
                     gp = gpar(fill = zcol[i]))
    }
}

levelplot(A1,xlab=NULL,ylab=NULL,
    at=do.breaks(c(0.5,1.01),101),
    panel=panel.corrgram.2,
    scales=list(x=list(rot=90)),
    colorkey=list(space="top"),
    col.regions=colorRampPalette(c("white","blue")), 
    main = "Corrgram of frequency of double-zeros")

#dataset.BIO[dataset.BIO == 0] <- NA
#dataset.BIO <- dataset.BIO_bk
```


##Data Normality 

Various statistical techniques assume normality. It is important to know whether the statistical technique to be used does assume normality, and what exactly is assumed to be normally distributed? For example, a *Principal component analysis* (PCA) does not require normality. *Linear regression* does assume normality, but is reasonably robust against violation of the assumption. For other techniques like *discriminant analysis*, normality of observations of a particular variable within each group is important. Therefore, testing for normality (among other things) is always recommanded when starting the data analysis phase of an ecological project.

###Quantile-Quantile (Q-Q) plots and et frequency histograms

For each variable, the first graph (left) illustrates by points the observed distribution of taxon values and the theoretical normal distribution calculated from the parameters of the distribution observed by a line. The more the values observed are positioned on the right, the more these are distributed according to the normal law. The second graph (right) shows a histogram the frequency distribution of the values observed by taxon. It allows to verify and validate if the data distribution seems to follow the normal distribution. This histogram also illustrates the mean abundance per taxon by a solid line as well as the standard deviation of abundances per taxon by two dotted lines.

```{r 13, echo=FALSE}
#Q-Q plots and frequency distribution histograms

for (i in 1:ncol(dataset.BIO)){
norm.mean <- mean(dataset.BIO[,i], na.rm=T)
norm.sd <- sd(dataset.BIO[,i], na.rm=T)
norm.n <- length(which(!is.na(dataset.BIO[,i])))
norm.bin <- ceiling(max(dataset.BIO[,i], na.rm=T)- min(dataset.BIO[,i], na.rm=T) /nclass.Sturges(which(!is.na(dataset.BIO[,i]))))

par(mfrow = c(1,2), mar=c(4,4,3,1))

qqnorm(dataset.BIO[,i], main = paste("Q-Q Plot\n", colnames(dataset.BIO[i])))
qqline(dataset.BIO[,i],lty = 2)

hist.ab <- hist(as.numeric(unlist(dataset.BIO[,i])), breaks = "Sturges", xlab = "Abondance", ylab = "Fréquence", main = paste("Frequency Distribution\n", colnames(dataset.BIO[i])))
xfit <- seq(min(which(!is.na(dataset.BIO[,i]))), max(which(!is.na(dataset.BIO[,i]))), length=norm.n)
yfit_density <- dnorm(xfit, mean = norm.mean, sd=norm.sd)
yfit_freq <- yfit_density*diff(hist.ab$mids[1:2])*norm.n
lines(xfit, yfit_freq, col="red", lwd=1)
  abline(v = norm.mean, col = "blue") +
  abline(v = norm.mean + norm.sd, lty = 2, col = "blue") +
  abline(v = norm.mean - norm.sd, lty = 2, col = "blue")
  }

```


####Normality test by taxon

One way to examine the normality of data is to use the Snows test. For each taxon, this calculation tests the null hypothesis that the data comes from an exact normal population.

This is a much less interesting null hypothesis than what we usually want, which is to know if the data come from a distribution that is similar enough to the normal to use normal theory inference.

A value of P (p-value) less than 0.05 indicates that it is not possible to assume that the distribution of the data follows the normal distribution with a probability of 95%. 

```{r 14, echo=FALSE, warning=FALSE}
#Tests de normalité de Snows par variable 

apply(dataset.BIO, 2, SnowsPenultimateNormalityTest)

```


####Taxa Occurences

At how many sites does each taxon occur?

First, the sorted list below shows the accreasing occurence of taxa in the dataset.

After that, two graphs are proposed. The first graph (left) shows the distribution of taxa presence. The second graph (right) illustrates the log-transformed data. On these graphs, the mean occurence is shown by a solid line whereas standard deviation by dashed lines.

En examination of these two graphs allows us to verify if the taxon dataset seams to comply to a normal distribution. If the distribution in the second graph is closer to a normal distribution, a log transformation could be useful.

```{r 15, echo=FALSE, warning=FALSE}
#Histograms for taxa occurences

spe.pres <- apply(dataset.BIO > 0, 2, sum, na.rm = TRUE)
spe.pres
print("Sorted list of taxa occurences")
sort(spe.pres)
spe.pres.mean <- mean(spe.pres, na.rm = T)
spe.pres.sd <- sd(spe.pres, na.rm = T)

pres.n <- length(which(!is.na(spe.pres)))

pres.bin <- ceiling((max(spe.pres)- min(spe.pres))/nclass.Sturges(spe.pres))

graph.pres1 <- qplot(spe.pres,
      geom = "histogram",
      binwidth = pres.bin,
      xlab = "Occurrence",
      ylab = "Number of taxa",
      main = "Number of occurrences",
      fill = I('lightsteelblue1'),
      color = I('black')) + 
  geom_vline(xintercept = spe.pres.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(spe.pres.mean + spe.pres.sd, spe.pres.mean - spe.pres.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = spe.pres.mean, sd = spe.pres.sd, n = pres.n, bw = pres.bin),
    lwd = 1,
    col = 'red')

graph.pres2 <- qplot(spe.pres,
      geom = "histogram",
      binwidth = pres.bin,
      xlab = "Occurrence",
      ylab = "Log(Number of taxa)",
      main = "Number of occurrences",
      fill = I('lightsteelblue1'),
      color = I('black'),
      log = 'y') + 
  geom_vline(xintercept = spe.pres.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(spe.pres.mean + spe.pres.sd, spe.pres.mean - spe.pres.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = spe.pres.mean, sd = spe.pres.sd, n = pres.n, bw = pres.bin),
    lwd = 1,
    col = 'red')

grid.arrange(graph.pres1, graph.pres2, ncol = 2)

```


The following graphs are similar to the previous ones but for relative frequencies.

```{r 16, echo=FALSE, warning=FALSE}
#Histograms for taxa relative frequencies

spe.relf <- 100*spe.pres/nrow(dataset.BIO)
spe.relf
print("Sorted list of taxa relative frequency")
round(sort(spe.relf), 1)
spe.relf.mean <- mean(spe.relf)
spe.relf.sd <- sd(spe.relf)

relf.n <- length(which(!is.na(spe.relf)))

relf.bin <- ceiling((max(spe.relf)- min(spe.relf))/nclass.Sturges(spe.relf))

graph.relf1 <- qplot(spe.relf,
      geom = "histogram",
      binwidth = relf.bin,
      xlab = "Frequency of occurrence (%)",
      ylab = "Number of taxa",
      main = "Taxa Relative Frequencies",
      fill = I('lightsteelblue1'),
      color = I('black')) + 
  geom_vline(xintercept = spe.relf.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(spe.relf.mean + spe.relf.sd, spe.relf.mean - spe.relf.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = spe.pres.mean, sd = spe.pres.sd, n = relf.n, bw = relf.bin),
    lwd = 1,
    col = 'red')

graph.relf2 <- qplot(spe.relf,
      geom = "histogram",
      binwidth = relf.bin,
      xlab = "Frequency of occurrence (%)",
      ylab = "Log(Number of taxa)",
      main = "Taxa Relative Frequencies (log)",
      fill = I('lightsteelblue1'),
      color = I('black'),
      log = "y") + 
  geom_vline(xintercept = spe.relf.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(spe.relf.mean + spe.relf.sd, spe.relf.mean - spe.relf.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = spe.pres.mean, sd = spe.pres.sd, n = relf.n, bw = relf.bin),
    lwd = 1,
    col = 'red')

grid.arrange(graph.relf1, graph.relf2, ncol = 2)

```


####Boxplots for taxa occurences

The first boxplot (left) illustrates the distribution of occurrences calculated from the data file whereas the second (right) shows the distribution of occurrences calculated from the log transformed data file.



```{r 17, echo=FALSE}
#Boxplot for taxa occurences

box.pres1 <- qplot(y = spe.pres, x = "", geom = "boxplot", ylab = "Occurrence", main = "Distribution of taxa occurences") + 
  theme(plot.title = element_text(hjust = 0.5))
box.pres2 <- qplot(y = spe.pres, x = "", geom = "boxplot", ylab = "Log(Occurrence)", main = "Distribution of taxa occurrences", log = "y") + 
  theme(plot.title = element_text(hjust = 0.5))

grid.arrange(box.pres1, box.pres2, ncol = 2)

```

The following boxplots are similar to the previous but for relative frequencies

```{r 18, echo=FALSE}
#Boîtes à moustaches de fréquences relatives en taxons

box.relf1 <- qplot(y = spe.relf, x = "", geom = "boxplot", ylab = "Fréquence d'occurrence (%)", main = "Distribution des fréquences\nrelatives en taxons") + 
  theme(plot.title = element_text(hjust = 0.5))
box.relf2 <- qplot(y = spe.relf, x = "", geom = "boxplot", ylab = "Log(Fréquence d'occurrence (%))", main = "Distribution des fréquences\nrelatives en taxons", log = "y") + 
  theme(plot.title = element_text(hjust = 0.5))

grid.arrange(box.relf1, box.relf2, ncol = 2)

```


##Taxa Independence

###Correlation matrix between taxa

The following correlation matrix shows the relationship between all pairs of taxa. It allows to verify the presence of co-occurence between taxa. Only taxa present in more than 25% of the visits are shown. 

```{r 19, echo=FALSE}
#Matrice de corrélation entre les variables - taxons avec fréquence > 25%

GGally::ggpairs(Benthos25, progress=F)
for(i in 1:ceiling(length(Benthos25)/4)){for(j in 1:ceiling(length(Benthos25)/4)){print(ggduo(Benthos25, columnsX=((i*4-3):(i*4))[((i*4-3):(i*4))<=length(Benthos25)], columnsY=((j*4-3):(j*4))[((j*4-3):(j*4))<=length(Benthos25)]))}}
```


###Pearson Correlation Matrix

The following correlation matrix illustrates the correlation strength that exists between two taxa by the intensity of the color. A correlation illustrated by the white color indicates a correlation force that tends to a value of 0 and a correlation illustrated by the dark blue color indicates a correlation force that tends to a value of 1. Pearson correlation values between taxa are shown in the lower left portion of the figure. This matrix also illustrates the proportion of non-zero abundance values that a taxon has with respect to all the abundance values observed in the latter. Only taxa present in more than 25% of the visits are shown.

```{r 20, echo=FALSE}
#Pearson Correlation Matrix - taxa with a frequency > 25%

corrgram(Benthos25, order = FALSE, lower.panel = panel.cor, upper.panel = panel.pie, text.panel = panel.txt, main=paste("Taxa Covariance - for frequency >", FreqMin, "%"))

```


###Taxa Abundance through Time

It could be interesting to look at the abundance of taxa through time to see dynamics or abrupt changes.

Pour chaque taxon, the first graph (top left) shows the abundance value of the data by sampling years. The second graph (top right) illustrates the observed patterns of changes in abundance of individuals over time. The third graph (bottom left) illustrates future forecasts of changes in abundance of individuals over time. On this graph, the blue line corresponds to the expected average trend of changes over time, the dark gray zone corresponds to a confidence interval of 80% and the pale gray zone corresponds to a 95% confidence interval. The fourth graph (bottom right) illustrates time series autocorrelation (ACF). An autocorrelation value greater than the 95% confidence interval illustrated by the dotted line indicates a possible dependency between the variable and the time of year (time). For example, a certain value of abundance of a taxon observed in a given year could be explained by a certain event dating from a previous year (lag in time in years). It should be noted that the autocorrelation at offset time 0 is, by definition, equal to 1.

```{r 21, echo=FALSE}
#Temporal independence of taxa

dataset.BIO2 <- left_join(dataset.BIO2, dataset.NAM2[,c("SampleId", "SampleDate")], by = "SampleId")

#dataset.BIO2_bk <- dataset.BIO2
#dataset.BIO2[is.na(dataset.BIO2)] <- 0

for(i in 2:(ncol(dataset.BIO2)-5)){
  par(mfrow = c(2, 2), mar = c(4,4,4,1))
  graph.time <- plot(dataset.BIO2$SampleDate, dataset.BIO2[,i], xlab = "Year", ylab = "Abundance", main = paste("Temporal Abundance of\n", colnames(dataset.BIO2[i])))

  graph.time2 <- ts(dataset.BIO2[,i], frequency = 12, start = c(2005-01-01), end = c(2016-01-01))
  plot(graph.time2, xlab = "Year", ylab = "Abundance", main = paste("Prediction of Abundance of\n", colnames(dataset.BIO2[i])))

  auto.arima <- auto.arima(graph.time2)
  graph.auto.arima <- plot(forecast(auto.arima, h = 120))
  
  graph.lag <- acf(graph.time2, xlab = "Lag (years)", ylab = "Abundance", main = paste("ACF for", colnames(dataset.BIO2[i])), ylim = c(0, 1))
}

#dataset.BIO2[dataset.BIO2 == 0] <- NA
#dataset.BIO2 <- dataset.BIO2_bk

```

The following results present the calculation of the Box-Ljung statistical test applied to each taxon and are complementary to the previous graphs. A value of P (p-value) lower than 0.05 indicates that the residual values of a variable depend on the period of the year (time).

```{r 22, echo=FALSE}
#Indépendance temporelle des taxons (suite)

#dataset.BIO2_bk <- dataset.BIO2
#dataset.BIO2[is.na(dataset.BIO2)] <- 0

for(i in 2:(ncol(dataset.BIO2)-5)){
  graph.time2 <- ts(dataset.BIO2[,i], frequency = 12, start = c(2005-01-01), end = c(2016-01-01))
  auto.arima <- auto.arima(graph.time2)
  box <- lapply(1:20, function(j) Box.test (resid(auto.arima), lag = j, type="Ljung"))
  print(paste(colnames(dataset.BIO2[i])))  
  print(box)
}

#dataset.BIO2 <- dataset.BIO2_bk

```


###Spatial Correlation

The following results present the spatial autocorrelation results applied to each variable. A value of P (p-value) less than 0.05 makes it possible to assume that the spatial distribution of the values is subject to non-random spatial aggregation. When the value of P (p-value) is less than 0.05, a positive Moran index (Moran's I) indicates that the values are aggregated with each other while a negative Moran index indicates that the values are scattered between them.

```{r 23, echo=FALSE}
##Spatial indépendence of taxa

#dataset.BIO2[is.na(dataset.BIO2)] <- 0

bio.dists <- as.matrix(dist(cbind(dataset.BIO2$Latitude, dataset.BIO2$Longitude)))
bio.dists[which(sapply(bio.dists, is.na))] <- 0 
bio.dists.inv <- 1/bio.dists
diag(bio.dists.inv) <- 0
bio.dists.inv[is.infinite(bio.dists.inv)] <- 0
lw <- mat2listw(bio.dists.inv)
lwW <- nb2listw(lw$neighbours, glist=lw$weights, style="W", zero.policy = T)

for(j in 2:(ncol(dataset.BIO2)-5)){
print(paste(colnames(dataset.BIO2[j])))
moran.test <- moran.test(dataset.BIO2[,j], lwW, alternative="two.sided", zero.policy = T) 
print(moran.test)
}

#dataset.BIO2 <- dataset.BIO2_bk

```


##Diversity

###Diversity Metrics

The following table presents the calculation of several diversity metrics per visit. 

```{r 24, echo=FALSE}
#Table for diversity metrics

#Richness
N0 <- rowSums(dataset.BIO > 0, na.rm = TRUE)

#Shannon's entropy
#dataset.BIO[is.na(dataset.BIO)] <- 0
H <- diversity(dataset.BIO)

#Shannon's index
N1 <- exp(H)

#Simpson's index
N2 <- diversity(dataset.BIO, "inv")

#Pielou's evenness
J <- H/log(N0)

#Shannon's evenness (Hill's ratio)
E10 <- N1/N0

#Simpson's evenness (Hill's ratio)
E20 <- N2/N0

#Table for diversity
#(div <- data.frame(N0, H, N1, N2, E10, E20, J))
#dataset.BIO[dataset.BIO == 0] <- NA

div <- data.frame(N0, H, N1, N2, J, E10, E20, dataset.BIO2$Site_Date_Number)
#div <- rownames_to_column(div, var = "rownames")
#div <- left_join(div, dataset.NAM2[,c("SampleId", "Site_Date_Number")], by = "SampleId")
#div$rownames <- NULL

{div <- div[, c(8,1,2,3,4,5,6,7)]
#div
names(div)[1] <- "Site_Date_Visit"
names(div)[2] <- "Richness"
names(div)[3] <- "Shannon's entropy"
names(div)[4] <- "Shannon's index"
names(div)[5] <- "Simpson's index"
names(div)[6] <- "Pielou's evenness"
names(div)[7] <- "Shannon's evenness"
names(div)[8] <- "Simpson's evenness"
}

datatable(div, filter = "top", rownames=F, options = list(scrollX = TRUE ))

rm(dataset.NAM2)
```


###Graphs for diversity


```{r 25, echo=FALSE}
#Histograms of Shannon's diversity index

#N1
#print("Ordre croissant des occurrences de la diversité de Shannon")
#round(sort(N1), 1)
N1.mean <- mean(N1)
N1.sd <- sd(N1)

N1.n <- length(which(!is.na(N1)))

N1.bin <- ceiling((max(N1)- min(N1))/nclass.Sturges(N1))

graph.N1.1 <- qplot(N1,
      geom = "histogram",
      binwidth = N1.bin,
      xlab = "Shannon's diversity",
      ylab = "Occurrence",
      main = "Shannon's diversity\n(observed)",
      fill = I('lightsteelblue1'),
      color = I('black')) + 
  geom_vline(xintercept = N1.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(N1.mean + N1.sd, N1.mean - N1.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = N1.mean, sd = N1.sd, n = N1.n, bw = N1.bin),
    lwd = 1,
    col = 'red')

graph.N1.2 <- qplot(N1,
      geom = "histogram",
      binwidth = N1.bin,
      xlab = "Shannon's diversity",
      ylab = "Log(Occurrence)",
      main = "Shannon's diversity\n(log-transformed)",
      fill = I('lightsteelblue1'),
      color = I('black'),
      log = "y") + 
  geom_vline(xintercept = N1.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(N1.mean + N1.sd, N1.mean - N1.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = N1.mean, sd = N1.sd, n = N1.n, bw = N1.bin),
    lwd = 1,
    col = 'red')

grid.arrange(graph.N1.1, graph.N1.2, ncol = 2)

#Histograms of Simpson's diversity index

#N2
#print("Ordre croissant des occurrences de la diversité de Simpson")
#round(sort(N2), 1)
N2.mean <- mean(N2)
N2.sd <- sd(N2)

N2.n <- length(which(!is.na(N2)))

N2.bin <- ceiling((max(N2)- min(N2))/nclass.Sturges(N2))

graph.N2.1 <- qplot(N2,
      geom = "histogram",
      binwidth = N2.bin,
      xlab = "Simpson's diversity",
      ylab = "Occurrence",
      main = "Simpson's diversity\n(observed)",
      fill = I('lightsteelblue1'),
      color = I('black')) + 
  geom_vline(xintercept = N2.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(N2.mean + N2.sd, N2.mean - N2.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = N2.mean, sd = N2.sd, n = N2.n, bw = N2.bin),
    lwd = 1,
    col = 'red')

graph.N2.2 <- qplot(N2,
      geom = "histogram",
      binwidth = N2.bin,
      xlab = "Simpson's diversity",
      ylab = "Log(Occurrence)",
      main = "Simpson's diversity\n(log-transformed)",
      fill = I('lightsteelblue1'),
      color = I('black'),
      log = "y") + 
  geom_vline(xintercept = N2.mean,
              color = 'blue3', size = 1) +
  geom_vline(xintercept = c(N2.mean + N2.sd, N2.mean - N2.sd),
             color = 'blue3', 
             size = 1,
             linetype = 2) +
  theme(plot.title = element_text(hjust = 0.5)) +
  stat_function(fun = function(x, mean, sd, n, bw){
    n * bw * dnorm(x = x, mean = mean, sd = sd)},
    args = list(mean = N2.mean, sd = N2.sd, n = N2.n, bw = N2.bin),
    lwd = 1,
    col = 'red')

grid.arrange(graph.N2.1, graph.N2.2, ncol = 2)

```



##Releases Notes
**What's New, Updated, or Fixed in This Release**

***
![ACTION:](../../../Configuration/logo_new.png) New &nbsp;&nbsp;&nbsp;  ![ACTION:](../../../Configuration/logo_updated.png) Updated &nbsp;&nbsp;&nbsp;  ![ACTION:](../../../Configuration/logo_fixed.png) Fixed

***

**CABIN_vv_biology.Rmd Version 1.1 — February 14, 2018**

![ACTION:](../../../Configuration/logo_updated.png)   **Update** --- Reduce the numbers of procedures and change their organisation.

**CABIN_vv_biology.Rmd Version 1.0 — August 18, 2017**

![ACTION:](../../../Configuration/logo_new.png)   **Première version**.


***

Developped by [Martin Jean](mailto:martin.jean@canada.ca) and Evelyne Paquette-Boisclair


